Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey

Alan Wee-Chung Liew; Getamesay Haile Dagnaw; Muhammad Hassan Maqsood; Wencheng Yang; Xingshuai Dong; Xuefei Yin; Yanming Zhu

REVIEW 3 major objections 4 minor 275 references

Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This survey argues that explainable AI in biomedical imaging should be organized by imaging modality, and it builds a modality-indexed map of methods, metrics, and tools, including recent vision-language advances.

desk verdict A genuinely useful modality-centered XAI survey for biomedical imaging, but the 'comprehensive' claim is undercut by a thin search protocol and visible citation/typo errors that need a revision pass. read the letter →

arxiv 2507.07148 v1 pith:KHG2B44S submitted 2025-07-09 cs.CV cs.LG

Getamesay Haile Dagnaw , Yanming Zhu , Muhammad Hassan Maqsood , Wencheng Yang , Xingshuai Dong , Xuefei Yin , Alan Wee-Chung Liew This is my paper

classification cs.CVcs.LG

keywords explainableAIinterpretabilitydeeplearningbiomedicalimageanalysismodality-centeredtaxonomyvision-languagemodelsevaluationmetricsopen-sourceXAIframeworks

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper is a survey that tries to establish that explanations in biomedical image analysis cannot be one-size-fits-all: each imaging modality has its own interpretability requirements, so XAI methods should be mapped to modalities rather than listed generically. It proposes a three-part method taxonomy—visualization-based, non-visualization-based, and latent-based—and a modality-centered organization spanning X-ray, CT, MRI, ultrasound, PET/SPECT, optical, and microscopy images. It also wants to bring multimodal learning and vision-language models into the XAI survey conversation, a topic earlier reviews largely skipped. If the survey is right, a researcher or clinician could choose an explanation method by first asking what modality they are working in, then what task, then which method class. The paper also compiles open-source toolkits and evaluation metrics to support benchmarking and adoption.

What carries the argument

The load-bearing organizing device is the modality-centered taxonomy: a mapping that pairs each biomedical imaging modality with the XAI method families that have been applied to it. The taxonomy sits on top of a three-part method classification—visualization-based methods (CAM, gradient/backpropagation, perturbation, attention), non-visualization-based methods (example, concept, textual), and latent-based methods (t-SNE, UMAP). The taxonomy does the work of the survey's central argument: it turns the literature from a list of techniques into a modality-indexed map, and it is what lets the authors claim to reveal the distinct interpretability challenges and requirements of each modality.

What would settle it

Re-run the stated Boolean query across the five named databases with an explicit date range and record-screening counts. If the recovered set contains modality-relevant XAI papers absent from the survey's tables—especially in PET/SPECT and cytology, where the tables list only a handful of entries each—then the claim that the modality-centered map is comprehensive would be falsified. A simpler quantitative check is to count the entries per modality table and compare them with the distribution of papers returned by the search; the claim predicts those distributions should match.

Watch

Extended reading notes

Core claim

The central claim is that prior surveys of XAI in medical imaging fail on three counts: they lack modality-aware analysis, they miss recent multimodal and vision-language advances, and they give little practical guidance. The paper's proposed fix is a modality-centered taxonomy that aligns XAI techniques with specific imaging types—radiographic, CT, MRI, ultrasound, PET/SPECT, optical, microscopy, and multi-modality—and a three-way method taxonomy of visualization-based, non-visualization-based, and latent-based approaches. Within each modality, the paper catalogs representative studies and identifies the dominant explanation class, such as Grad-CAM variants for chest X-rays and mammography, LRP for SPECT, and concept-based methods for histopathology. It also surveys interpretable vision-language models and argues that explanation quality should be judged by alignment with clinical reasoning, not just visual plausibility.

Load-bearing premise

The survey's claim to be comprehensive rests on the assumption that its literature search—five databases and one example query, with no search dates, screening counts, or exclusion numbers—captured a representative slice of the XAI-in-biomedical-imaging literature.

Editorial extensions

If this is right

A clinician starting on a new modality can shortcut method selection: for 2D radiographic tasks the survey points to CAM-family heatmaps as the default, while for PET/SPECT and fMRI it points to LRP, SHAP, and latent-space visualization as more appropriate.
Researchers working on multimodal fusion now have a named gap: current XAI lacks mechanisms to quantify each modality's contribution, so fusion-aware attribution is an open direction rather than a solved problem.
The evaluation-metrics summary implies that spatial metrics (relevance mass accuracy, deletion/insertion, pointing game) and text metrics (BLEU, METEOR, ROUGE, CIDEr, SPICE) should be chosen by explanation type, giving heatmap and captioning papers a ready benchmarking vocabulary beyond accuracy.
Interpretable vision-language models, such as concept bottlenecks and prototype-based reasoning in chest X-rays and dermatology, are positioned as the next frontier; if the survey's synthesis is correct, this area will grow faster than post-hoc saliency methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The authors leave implicit that the modality-centered map doubles as a spurious-correlation detector: if one method family dominates a modality for convenience rather than clinical need, the uneven distribution across tables exposes that pattern.
The taxonomy could be tested by building a recommendation engine that takes a modality and task pair and suggests methods based on the survey's mappings, then comparing those suggestions against expert-selected methods on a shared benchmark.
A testable extension is to quantify coverage by modality from the reference lists: if PET/SPECT and cytology entries are far fewer than chest X-ray entries, that is a measure of the field's immaturity, not just the survey's scope.
The paper's emphasis on clinical alignment suggests a practical evaluation protocol: ask radiologists and pathologists whether highlighted regions match their diagnostic landmarks, then compare those ratings across modalities to give the modality-centered claim an empirical basis it currently lacks.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This survey is worth a serious referee, but the referee should send it back for a careful revision rather than accept it as is. The best parts are the modality-organized application tables (CXR, CT, MRI, ultrasound, PET/SPECT, optical, microscopy, multi-modal) and the short section on interpretable vision-language models, which is the freshest content and goes beyond most prior surveys. The three-way taxonomy (visual, non-visual, latent) is a reasonable repackaging rather than a new framework, and the authors are upfront about that. The tool and metric summaries are practical and will help practitioners get oriented quickly. On the positive side, the writing is clear, the organization is sensible, and the coverage of the literature is broad even if not fully systematic.

The soft spots are real but fixable. The literature search in Section 1.3 gives no search dates, no record counts, no screening numbers, no PRISMA-style flow, so a reader cannot independently verify the "comprehensive" claim. The concern about representativeness is legitimate, especially for underrepresented modalities like PET/SPECT and cytology. More damning are the concrete errors: ProbCBM is attributed to [253], which is actually the Post-hoc CBM paper; references [192] and [193] are the same Samek et al. 2016 paper; and the tables contain typos like 'Beast cancer', 'lassification', and 'egmentation'. These are exactly the kind of errors that make a survey unreliable as a citation source until corrected. They do not fatally undermine the central message, but they do mean the reader's confidence in the modality tables should be moderate until the authors clean up the reference list and table entries.

The evaluation metrics section is serviceable but a bit thin; it mostly restates standard definitions without much critical comparison in the biomedical context. I would have liked more discussion of when these metrics mislead in clinical settings.

Who benefits: practitioners and newcomers who want a modality-aware map of XAI methods, tools, and metrics, and researchers looking for a starting point on interpretable VLMs in medical imaging. If the authors fix the search reporting, deduplicate references, and correct the misattributions, this becomes a solid reference survey. I would not desk-reject it, but I would not accept it without those revisions. Send it to peer review, yes, and ask for a revision.

Off the record: I'd probably still cite the VLM section and a few modality tables after the revision, but I'd double-check every reference before citing.

Referee Report

3 major / 4 minor

Summary. This manuscript presents a survey of explainable AI (XAI) methods applied to biomedical image analysis. It offers a three-part taxonomy of XAI techniques (visualization-based, non-visualization-based, and latent-based), then organizes applications by imaging modality (radiographic, CT, MRI, ultrasound, PET/SPECT, optical, microscopy, and multi-modality). It also includes a section on interpretable vision-language models, a list of open-source XAI frameworks, an overview of evaluation metrics, and a discussion of open challenges. The authors claim that the survey fills a gap in prior reviews through its modality-centered perspective and coverage of recent VLM-based interpretability.

Significance. If accurate, this survey would be a valuable reference for researchers and practitioners: it consolidates a large and fragmented literature, provides a structured taxonomy, and highlights modality-specific interpretability requirements. The inclusion of recent vision-language model work and practical resources (frameworks and metrics) is a genuine strength that goes beyond several earlier surveys. The curated modality tables could serve as a practical entry point for method selection. However, the survey's value depends heavily on the reliability of its curated entries and the representativeness of its literature search, both of which currently have important gaps. The paper does not contain self-derived predictions or fitted parameters, so the main burden of assessment falls on the accuracy and completeness of the synthesis.

major comments (3)

[Section 1.3] The literature search protocol is under-specified. The authors name five databases and give one example Boolean query, but do not report the search date range, the number of records retrieved, the number screened, the inclusion/exclusion criteria beyond a brief sentence, or a PRISMA-style flow diagram. Without these elements, the claim of "comprehensive" coverage cannot be verified, and the modality tables (Tables 2-9) may be skewed toward popular modalities (e.g., CXR, MRI) while under-representing others (e.g., PET/SPECT, cytology). Please provide the search dates, record counts, and a screening flowchart, or temper the "comprehensive" claim accordingly.
[Section 2.2.2] The attribution of Probabilistic CBMs (ProbCBM) to reference [253] is incorrect. Reference [253] is Yuksekgonul et al.'s "Post-hoc Concept Bottleneck Models," whereas ProbCBM is a distinct method introduced by Kim et al. (2023), which models concepts as probability distributions. This misattribution undermines the reliability of the concept-based taxonomy, since the text describes the two methods as separate while citing the same source. Additionally, references [192] and [193] are the same paper (Samek et al., 2016), cited for both Deletion/Insertion and AOPC in Section 5.1; the duplication should be removed and the citations renumbered.
[Tables 2-9] The curated application tables contain numerous typos and transcription errors that reduce their utility as a reliable map of the literature. Examples include "Beast cancer" (Table 2, mammography), "lassification" (Table 2, digital tomosynthesis), "egmentation" (Table 2, fluoroscopy), "detection detection" (Table 7, fundus, LIME row), "lcassification" (Table 7, endoscopy, SHAP row), and "lassification" (Table 6, SPECT). These errors should be corrected in a careful proofreading pass; as they stand, they undermine confidence in the accuracy of the curated entries, which are a central contribution of the survey.

minor comments (4)

[Section 3.9 / Table 10] Some entries in Table 10 do not clearly represent interpretable vision-language models. For instance, the entry "CXR + Text [174] Grad-CAM" appears to be a Grad-CAM explanation rather than a VLM-specific interpretability method. Please clarify the selection criteria for this table and ensure each entry genuinely involves a vision-language component.
[Section 5.1] In the AOPC equation, the same symbol N is used for the number of images and the number of perturbation steps in the surrounding text, which can be confusing. Please use distinct notation for these two quantities.
[Figure 2] The taxonomy figure lists TraCe and ProtoType under example-based methods, but the text in Section 2.2.1 describes TraCe as a calibration-based counterfactual explainer. Consider whether this placement is optimal or add a clarifying note.
[General] Several minor textual issues appear throughout, such as "to to" in Section 2.1.1, the unclosed quote in the Boolean query in Section 1.3, and inconsistent capitalization of "CXR" versus "chest X-ray." A copyediting pass would improve readability.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the survey's claims are organizational and synthesis-based, and its few self-citations are not load-bearing.

full rationale

This is a survey, not a derivation. Its central claims—that prior reviews lack a modality-aware perspective and that this paper organizes XAI methods by imaging modality—are organizational statements, not predictions produced from fitted parameters or first-principles equations. The taxonomy in Section 2 and the modality tables in Section 3 are classifications of existing literature; they do not reduce to their own inputs by construction. The only self-citations ([268], [269], [270]) appear in the introduction as examples of deep-learning success and are not used to justify any load-bearing claim. Limitations such as the unreported search dates and screening counts in Section 1.3 and the visible reference errors (the ProbCBM description citing the PCBM paper [253], and the duplicate entries [192]/[193]) are substantive quality concerns for the 'comprehensive' claim, but they concern search representativeness and citation accuracy, not circular reasoning. No circular step can be exhibited with the required specificity, so the score is set to 1 only to acknowledge the minor, non-load-bearing self-citations.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The survey introduces no free parameters, no new mathematical axioms, and no invented entities. Its only load-bearing assumption is that the collected literature accurately represents the field.

assumptions (1)

domain assumption The selected literature corpus is representative of the full XAI-in-biomedical-imaging field.
Section 1.3 describes a search of five databases with one example Boolean query but no dates, screening counts, or PRISMA-style protocol; the survey's claim to comprehensiveness depends on this corpus being complete and unbiased.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey." pith.science (2026). https://pith.science/paper/KHG2B44S

@misc{pith2026250707148,
  author       = {Pith},
  title        = {Pith review of: Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KHG2B44S}},
  note         = {Machine review of arXiv:2507.07148}
}

read the original abstract

Explainable artificial intelligence (XAI) has become increasingly important in biomedical image analysis to promote transparency, trust, and clinical adoption of DL models. While several surveys have reviewed XAI techniques, they often lack a modality-aware perspective, overlook recent advances in multimodal and vision-language paradigms, and provide limited practical guidance. This survey addresses this gap through a comprehensive and structured synthesis of XAI methods tailored to biomedical image analysis.We systematically categorize XAI methods, analyzing their underlying principles, strengths, and limitations within biomedical contexts. A modality-centered taxonomy is proposed to align XAI methods with specific imaging types, highlighting the distinct interpretability challenges across modalities. We further examine the emerging role of multimodal learning and vision-language models in explainable biomedical AI, a topic largely underexplored in previous work. Our contributions also include a summary of widely used evaluation metrics and open-source frameworks, along with a critical discussion of persistent challenges and future directions. This survey offers a timely and in-depth foundation for advancing interpretable DL in biomedical image analysis.

Figures

Figures reproduced from arXiv: 2507.07148 by the authors.

**Figure 2.** A structured taxonomy of XAI methods in biomedical image analysis. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Grad-CAM workflow for generating class-specific visual explanations. Gradients of the target class score with respect to [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Illustration of two non-visual XAI paradigms in medical image analysis. (a) Example-based XAI: The input is compared in [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

**Figure 5.** Figure 5: Self-explaining VLM framework for medical image analysis via report-guided visual attribution. The input image and radiology [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

275 extracted references · 58 canonical work pages

[253]

Alan S. Young. 2022. AI in healthcare startups and special challenges. Intelligence-Based Medicine 6 (2022), 100050. doi:10.1016/j.ibmed.2022.100050

arXiv 2022
[192]

R Ricciardi, G Mettivier, M Staffa, A Sarno, G Acampora, S Minelli, A Santoro, E Antignani, A Orientale, IAM Pilotti, et al. 2021. A deep learning classifier for digital breast tomosynthesis. Physica Medica 83 (2021), 184–193

2021
[193]

Isabel Rio-Torto, Jaime S Cardoso, and Luis Filipe Teixeira. 2024. Parameter-Efficient Generation of Natural Language Explanations for Chest X-ray Classification. In Medical Imaging with Deep Learning

2024
[1]

Samira Abnar and Willem Zuidema. 2020. Quantifying Attention Flow in Transformers. In Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 4190–4197

2020
[2]

Md Faysal Ahamed, Md Nahiduzzaman, Md Rabiul Islam, Mansura Naznine, et al. 2024. Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AI. Expert Systems with Applications 256 (2024), 124908

2024
[3]

Naveed Ahmad, Jamal Hussain Shah, Muhammad Attique Khan, Jamel Baili, Ghulam Jillani Ansari, Usman Tariq, et al. 2023. A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI. Frontiers in Oncology 13 (June 2023). Manuscript submitted to ACM Explainable Artificial Intelligence in Biomedical Im...

2023
[4]

Shamim Ahmed, Sm Nuruzzaman Nobel, and Oli Ullah. 2023. An effective deep cnn model for multiclass brain tumor detection using mri images and shap explainability. In 2023 International Conference on Electrical, Computer and Communication Engineering . IEEE, 1–6

2023
[5]

Sarder Tanvir Ahmed, Shomtirtha Barua, Md Fahim-Ul-Islam, and Amitabha Chakrabarty. 2024. CoAtNet-Lite: Advancing Mammogram Mass Detection Through Lightweight CNN-Transformer Fusion with Attention Mapping. In International Conference on EEICT . IEEE, 143–148

2024

Show all 275 references

[6]

Md Manjurul Ahsan, Redwan Nazim, Zahed Siddique, and Pedro Huebner. 2021. Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME. Healthcare 9, 9 (Aug. 2021), 1099

2021
[7]

Arjun Akula, Shuai Wang, and Song-Chun Zhu. 2020. CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines.Proceedings of the AAAI Conference on Artificial Intelligence 34, 03 (April 2020), 2594–2601

2020
[8]

Sikandar Ali, Ali Hussain, et al. 2022. Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model. Sensors 22, 24 (Dec. 2022), 9983

2022
[9]

Mohammad Alsharid, Harshita Sharma, Lior Drukker, Pierre Chatelain, Aris T Papageorghiou, and J Alison Noble. 2019. Captioning ultrasound images automatically. In MICCAI. Springer, 338–346

2019
[10]

Peter Anderson, Basura Fernando, et al. 2016. Spice: Semantic propositional image caption evaluation. In ECCV. Springer, 382–398

2016
[11]

Plamen Angelov et al. 2022. Towards Explainable Deep Neural Networks for the Automatic Detection of Diabetic Retinopathy. Applied Sciences 12, 19 (Sept. 2022), 9435

2022
[12]

Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, et al. 2015. Vqa: Visual question answering. In ICCV. 2425–2433

2015
[13]

Tasnim Sakib Apon, Mohammad Mahmudul Hasan, Abrar Islam, and Md Golam Rabiul Alam. 2021. Demystifying deep learning models for retinal OCT disease classification using explainable AI. In IEEE Asia-Pacific Conference on Computer Science and Data Engineering . 1–6

2021
[14]

Leila Arras, Ahmed Osman, and Wojciech Samek. 2022. CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations. Information Fusion 81 (May 2022), 14–40

2022
[15]

Fahima Hasan Athina, Sadaf Ahmed Sara, Quazi Sabrina Sarwar, et al. 2022. Multi-classification network for detecting skin diseases using deep learning and XAI. In International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies . 648–655

2022
[16]

Paula Atim, Shereen Fouad, Sinling Tiffany Yu, Antonio Fratini, et al. 2024. Explainable deep learning framework for ground glass opacity (ggo) segmentation from chest ct scans. In International Conference on Medical Imaging and Computer-Aided Diagnosis . Springer, 187–197

2024
[17]

Muhammad Muzzammil Auzine, Maleika Heenaye-Mamode Khan, et al. 2023. Classification of gastrointestinal Cancer through explainable AI and ensemble learning. In International Conference of Women in Data Science at Prince Sultan University . IEEE, 195–200

2023
[18]

Hareem Ayesha, Sajid Iqbal, Mehreen Tariq, Muhammad Abrar, Muhammad Sanaullah, Ishaq Abbas, Amjad Rehman, Muhammad Farooq Khan Niazi, et al. 2021. Automatic medical image interpretation: State of the art and future directions. Pattern Recognition 114 (2021), 107856

2021
[19]

Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLOS ONE 10, 7 (2015), e0130140

2015
[20]

Satanjeev Banerjee and Alon Lavie. 2004. Meteor: an automatic metric for mt evaluation with high levels of correlation with human judgments. Proceedings of ACL-WMT (2004), 65–72

2004
[21]

Emre Celebi, et al

Catarina Barata, M. Emre Celebi, et al. 2021. Explainable skin lesion diagnosis using taxonomies. Pattern Recognition 110 (2021), 107413

2021
[22]

Konstantinos Barzas, Shereen Fouad, Gainer Jasa, et al. 2023. An explainable deep learning framework for mandibular canal segmentation from cone beam computed tomography volumes. In International Conference on Computational Advances in Bio and Medical Sciences . Springer, 1–13

2023
[23]

Djamila Romaissa Beddiar, Mourad Oussalah, Usman Muhammad, and Tapio Seppänen. 2023. A Deep learning based data augmentation method to improve COVID-19 detection from medical imaging. Knowledge-Based Systems 280 (2023), 110985

2023
[24]

Mohan Bhandari, Tej Bahadur Shahi, and Arjun Neupane. 2023. Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks. Journal of Imaging 9, 10 (2023), 219

2023
[25]

Mohan Bhandari, Tej Bahadur Shahi, Birat Siku, and Arjun Neupane. 2022. Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI. Computers in Biology and Medicine 150 (Nov. 2022), 106156

2022
[26]

Yequan Bie, Luyang Luo, Zhixuan Chen, and Hao Chen. 2024. Xcoop: Explainable prompt learning for computer-aided diagnosis via concept-guided context optimization. In International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 773–783

2024
[27]

Katarzyna Borys, Yasmin Alyssa Schmitt, Meike Nauta, Christin Seifert, Nicole Krämer, et al. 2023. Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches. European Journal of Radiology 162 (2023), 110786

2023
[28]

Friedrich, et al

Katarzyna Borys, Yasmin Alyssa Schmitt, Meike Nauta, Christin Seifert, Nicole Krämer, Christoph M. Friedrich, et al. 2023. Explainable AI in medical imaging: An overview for clinical practitioners – Saliency-based XAI approaches. European Journal of Radiology 162 (2023), 110787

2023
[29]

Luca Brunese, Francesco Mercaldo, Alfonso Reginelli, and Antonella Santone. 2020. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. Computer Methods and Programs in Biomedicine 196 (2020), 105608

2020
[30]

Phuoc-Nguyen Bui, Duc-Tai Le, Junghyun Bum, Seongho Kim, Su Jeong Song, and Hyunseung Choo. 2023. Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images. Bioengineering 10, 11 (2023), 1249

2023
[31]

Ateeq Ur Rehman Butt, Muhammad Asif, Tayyaba Rashid, et al. 2024. Beyond Boundaries: A Novel Ensemble Approach for Breast Cancer Detection in Ultrasound Imaging Using Deep Learning. In International Bhurban Conference on Applied Sciences and Technology . IEEE, 315–320

2024
[32]

Giovanna Castellano, Andrea Esposito, Eufemia Lella, Graziano Montanaro, and Gennaro Vessio. 2024. Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET. Scientific Reports 14, 1 (2024), 5210. Manuscript submitted to ACM 28 Getamesay et al

2024
[33]

Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian. 2018. Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. In W ACV. 839–847

2018
[34]

Hugh Chen, Scott Lundberg, and Su-In Lee. 2021. Explaining Models by Propagating Shapley Values of Local Components . Springer, Cham, 261–270

2021
[35]

Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. 2018. Learning to explain: An information-theoretic perspective on model interpretation. In International conference on machine learning . PMLR, 883–892

2018
[36]

Jui-Jen Chen, Ting-Yi Su, Wei-Shiang Chen, Yen-Hsiang Chang, et al. 2021. Convolutional neural network in the evaluation of myocardial ischemia from CZT SPECT myocardial perfusion imaging: comparison to automated quantification. Applied Sciences 11, 2 (2021), 514

2021
[37]

Zhihong Chen et al. 2024. Chexagent: Towards a foundation model for chest x-ray interpretation. arXiv preprint arXiv:2401.12208 (2024)

2024 arXiv
[38]

Mohamed Chetoui and Moulay A Akhloufi. 2021. Deep efficient neural networks for explainable COVID-19 detection on CXR images. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems . Springer, 329–340

2021
[39]

Javier Civit-Masot, Alejandro Bañuls-Beaterio, et al. 2022. Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques. Computer Methods and Programs in Biomedicine 226 (2022), 107108

2022
[40]

Edo Collins, Radhakrishna Achanta, and Sabine Süsstrunk. 2018. Deep Feature Factorization For Concept Discovery. In ECCV. 336–352

2018
[41]

Fuze Cong, Shibiao Xu, Li Guo, and Yinbing Tian. 2022. Caption-aware medical VQA via semantic focusing and progressive cross-modality comprehension. In Proceedings of the 30th ACM International Conference on Multimedia . 3569–3577

2022
[42]

Piotr Dabkowski et al. 2017. Real time image saliency for black box classifiers. Advances in neural information processing systems 30 (2017)

2017
[43]

Narayana Darapaneni et al. 2022. Explainable diagnosis, lesion segmentation and quantification of COVID-19 infection from CT images using convolutional neural networks. In IEEE Annual Information Technology, Electronics and Mobile Communication Conference . IEEE, 0171–0178

2022
[44]

Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, et al. 2017. Visual dialog. In IEEE conference on CVPR . 326–335

2017
[45]

Sasmitha Dasanayaka, Sanju Silva, Vimuth Shantha, Dulani Meedeniya, and Thanuja Ambegoda. 2022. Interpretable machine learning for brain tumor analysis using MRI. In International Conference on Advanced Research in Computing . IEEE, 212–217

2022
[46]

Lisa Anita De Santi, Filippo Bargagna, et al. 2023. Explainable CNN-Based Cardiac Amyloidosis Classification from PET Images Through Manifold Learning. In Mediterranean Conference on Medical and Biological Engineering and Computing . Springer, 491–503

2023
[47]

Ramaswamy

Saurabh Desai and Harish G. Ramaswamy. 2020. Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization. In 2020 IEEE Winter Conference on Applications of Computer Vision (W ACV). 972–980

2020
[48]

Nilkanth Mukund Deshpande, Shilpa Gite, and Biswajeet Pradhan. 2024. Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model. International Journal on Smart Sensing and Intelligent Systems 1 (2024)

2024
[49]

Simon Sherratt

Tribikram Dhar, Nilanjan Dey, Surekha Borra, and R. Simon Sherratt. 2023. Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust. IEEE Transactions on Technology and Society 4, 1 (2023), 68–75

2023
[50]

Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Advances in neural information processing systems 31 (2018)

2018
[51]

Songhui Diao, Weiren Luo, Jiaxin Hou, Ricardo Lambo, Hamas A Al-Kuhali, Hanqing Zhao, Yinli Tian, et al . 2023. Deep multi-magnification similarity learning for histopathological image classification. IEEE Journal of Biomedical and Health Informatics 27, 3 (2023), 1535–1545

2023
[52]

Marion Dörrich, Markus Hecht, Rainer Fietkau, Arndt Hartmann, Heinrich Iro, Antoniu-Oreste Gostian, Markus Eckstein, and Andreas M. Kist
[53]

Rachel Lea Draelos et al. 2021. Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks. arXiv:2011.08891

2021 arXiv
[54]

Rachel Lea Draelos et al. 2022. Explainable multiple abnormality classification of chest CT volumes. Artif. Intell. Med. 132 (2022), 102372

2022
[55]

Furui Duan, Minghui Zhang, et al . 2025. Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From 18F-FDG PET/CT Based on Interpretable Machine Learning. Academic Radiology 32, 3 (2025), 1645–1655

2025
[56]

Dunnmon, Darvin Yi, Curtis P

Jared A. Dunnmon, Darvin Yi, Curtis P. Langlotz, Christopher Ré, Daniel L. Rubin, and Matthew P. Lungren. 2019. Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. Radiology 290, 2 (2019), 537–544

2019
[57]

Vanessa Gonzalez Duque, Alexandra Marquardt, Yordanka Velikova, et al. 2024. Ultrasound segmentation analysis via distinct and completed anatomical borders. International Journal of Computer Assisted Radiology and Surgery 19, 7 (2024), 1419–1427

2024
[58]

Sara El-Ateif, Ali Idri, and José Luis Fernández-Alemán. 2024. On the differences between CNNs and vision transformers for COVID-19 diagnosis using CT and chest x-ray mono-and multimodality. Data Technologies and Applications 58, 3 (2024), 517–544

2024
[59]

Ismail Elbouknify, Afaf Bouhoute, Khalid Fardousse, Ismail Berrada, and Abdelmajid Badri. 2023. CT-xCOV: a CT-scan based Explainable Framework for COVid-19 diagnosis. In 2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM) . IEEE, 1–8

2023
[60]

Charles A Ellis, Robyn L Miller, and Vince D Calhoun. 2023. Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches. Informatics in medicine unlocked 37 (2023), 101176

2023
[61]

Zhengqing Fang, Kun Kuang, Yuxiao Lin, Fei Wu, and Yu-Feng Yao. 2020. Concept-based explanation for fine-grained images and its application in infectious keratitis classification. In Proceedings of the 28th ACM international conference on Multimedia . 700–708

2020
[62]

Ahmeed Suliman Farhan, Muhammad Khalid, and Umar Manzoor. 2025. XAI-MRI: an ensemble dual-modality approach for 3D brain tumor segmentation using magnetic resonance imaging. Frontiers in Artificial Intelligence 8 (2025), 1525240

2025
[63]

Aya Farrag, Gad Gad, Zubair Md Fadlullah, Mostafa M Fouda, and Maazen Alsabaan. 2023. An Explainable AI System for Medical Image Segmentation With Preserved Local Resolution: Mammogram Tumor Segmentation. IEEE Access (2023)

2023
[64]

Ruigang Fu et al. 2020. Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs. arXiv:2008.02312 [cs.CV] Manuscript submitted to ACM Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey 29

2020 arXiv
[65]

Gaurav O Gajbhiye et al. 2020. Automatic report generation for chest x-ray images: a multilevel multi-attention approach. In CVIP. 174–182

2020
[66]

Biswarup Ganguly, Debangshu Dey, and Sugata Munshi. 2022. An Explainable Convolutional Neural Network-Based Method for Skin-Lesion Classification from Dermoscopic Images. 279–291 pages. doi:10.1002/9781119861850.ch16

2022 doi
[67]

Yunhe Gao, Difei Gu, Mu Zhou, and Dimitris Metaxas. 2024. Aligning human knowledge with visual concepts towards explainable medical image classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 46–56

2024
[68]

Rogelio García-Aguirre, Luis Torres-Treviño, Eva María Navarro-López, and José Alberto González-González. 2022. Towards an interpretable model for automatic classification of endoscopy images. In Mexican International Conference on Artificial Intelligence . Springer, 297–307

2022
[69]

Alessia Gerbasi, Greta Clementi, Fabio Corsi, Sara Albasini, Alberto Malovini, Silvana Quaglini, and Riccardo Bellazzi. 2023. DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms. Comput. Methods Programs Biomed. 235 (2023), 107483

2023
[70]

Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim. 2019. Towards automatic concept-based explanations. NeurIPS 32 (2019)

2019
[71]

Mara Giavina-Bianchi, William Gois Vitor, Victor Fornasiero de Paiva, et al. 2023. Explainability agreement between dermatologists and five visual explanations techniques in deep neural networks for melanoma AI classification. Frontiers in Medicine 10 (Aug. 2023)

2023
[72]

Neelankit Gautam Goswami, Niranjana Sampathila, Giliyar Muralidhar Bairy, Anushree Goswami, et al. 2024. Explainable artificial intelligence and deep learning methods for the detection of sickle cell by capturing the digital images of blood smears. Information 15, 7 (2024), 403

2024
[73]

Yash Goyal, Amir Feder, Uri Shalit, and Been Kim. 2019. Explaining classifiers with causal concept effect (cace). arXiv:1907.07165 (2019)

2019 arXiv
[74]

Riccardo Guidotti, Anna Monreale, Stan Matwin, and Dino Pedreschi. 2020. Black box explanation by learning image exemplars in the latent feature space. In Machine Learning and Knowledge Discovery in Databases: European Conference . 189–205

2020
[75]

Chen Guo, Minzhong Yu, and Jing Li. 2021. Prediction of Different Eye Diseases Based on Fundus Photography via Deep Transfer Learning.Journal of Clinical Medicine 10, 23 (Nov. 2021), 5481. doi:10.3390/jcm10235481

2021 doi
[76]

Ke Han, Yue Yu, and Tao Lu. 2024. Transfer Learning and Interpretable Analysis-Based Quality Assessment of Synthetic Optical Coherence Tomography Images by CGAN Model for Retinal Diseases. Processes 12, 1 (Jan. 2024), 182. doi:10.3390/pr12010182

2024 doi
[77]

Rezuana Haque, Md Mehedi Hassan, Anupam Kumar Bairagi, and Sheikh Mohammed Shariful Islam. 2024. NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data. Scientific Reports 14, 1 (2024), 1524

2024
[78]

Akshay Harikumar, Simi Surendran, and S Gargi. 2024. Explainable AI in Deep Learning Based Classification of Fetal Ultrasound Image Planes. Procedia Computer Science 233 (2024), 1023–1033

2024
[79]

Md Mahmodul Hasan, Muhammad Minoar Hossain, Mohammad Motiur Rahman, AKM Azad, et al. 2023. FP-CNN: Fuzzy pooling-based convolutional neural network for lung ultrasound image classification with explainable AI. Computers in Biology and Medicine 165 (2023), 107407

2023
[80]

Zhu He, Mingwei Lin, Zeshui Xu, Zhiqiang Yao, Hong Chen, et al. 2022. Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture. Information Sciences 608 (2022), 1093–1112

2022
[81]

Thao Thi Ho, Geun-Tae Kim, Taewoo Kim, Sanghun Choi, and Eun-Kee Park. 2022. Classification of rotator cuff tears in ultrasound images using deep learning models. Medical & Biological Engineering & Computing 60, 5 (2022), 1269–1278

2022
[82]

MD Imran Hossain, Ghada Zamzmi, Peter R Mouton, MD Sirajus Salekin, Yu Sun, and Dmitry Goldgof. 2025. Explainable AI for medical data: current methods, limitations, and future directions. Comput. Surveys 57, 6 (2025), 1–46

2025
[83]

Shahriar Hossain, Amitabha Chakrabarty, Thippa Reddy Gadekallu, et al. 2024. Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification. IEEE J. Biomed. Health Inform. 28, 3 (2024), 1261–1272

2024
[84]

Nussair Adel Hroub, Ali Nader Alsannaa, Maad Alowaifeer, Motaz Alfarraj, and Emmanuel Okafor. 2024. Explainable deep learning diagnostic system for prediction of lung disease from medical images. Computers in Biology and Medicine 170 (March 2024), 108012

2024
[85]

Sardar Mehboob Hussain, Domenico Buongiorno, Nicola Altini, Francesco Berloco, Berardino Prencipe, et al. 2022. Shape-based breast lesion classification using digital tomosynthesis images: The role of explainable artificial intelligence. Applied Sciences 12, 12 (2022), 6230

2022
[86]

Tahir Hussain and Hayaru Shouno. 2023. Explainable deep learning approach for multi-class brain magnetic resonance imaging tumor classification and localization using gradient-weighted class activation mapping. Information 14, 12 (2023), 642

2023
[87]

Ta Duc Huy, Sen Kim Tran, Phan Nguyen, Nguyen Hoang Tran, Tran Bao Sam, Anton van den Hengel, Zhibin Liao, Johan W Verjans, Minh-Son To, and Vu Minh Hieu Phan. 2025. Interactive Medical Image Analysis with Concept-based Similarity Reasoning. In cvpr. 30797–30806

2025
[88]

Agughasi Victor Ikechukwu, S Murali, and B Honnaraju. 2023. COPDNet: an explainable ResNet50 model for the diagnosis of COPD from CXR images. In 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON) . IEEE, 1–7

2023
[89]

Md Khairul Islam et al. 2023. Enhancing lung abnormalities detection and classification using a Deep Convolutional Neural Network and GRU with explainable AI: A promising approach for accurate diagnosis. Machine Learning with Applications 14 (2023), 100492

2023
[90]

Nazmul Islam, Md

Md. Nazmul Islam, Md. Golam Rabiul Alam, Tasnim Sakib Apon, Md. Zia Uddin, et al. 2023. Interpretable Differential Diagnosis of Non-COVID Viral Pneumonia, Lung Opacity and COVID-19 Using Tuned Transfer Learning and Explainable AI. Healthcare 11, 3 (Jan. 2023), 410

2023
[91]

Md Nazmul Islam, Mehedi Hasan, Md Kabir Hossain, Md Golam Rabiul Alam, Md Zia Uddin, and Ahmet Soylu. 2022. Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography. Scientific Reports 12, 1 (2022), 11440

2022
[92]

Md Rakibul Islam, Md Mahbubur Rahman, et al. 2024. Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images. Machine Learning with Applications 16 (2024), 100555

2024
[93]

Oahidul Islam, Md Assaduzzaman, and Md Zahid Hasan. 2024. An explainable AI-based blood cell classification using optimized convolutional neural network. Journal of Pathology Informatics 15 (2024), 100389. Manuscript submitted to ACM 30 Getamesay et al

2024
[94]

Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga, Dan Jørgensen, Ole Winther, and Alfiia Galimzianova. 2023. DermX: An end-to-end framework for explainable automated dermatological diagnosis. Medical Image Analysis 83 (Jan. 2023), 102647. doi:10.1016/j.media.2022.102647

2023
[95]

Xun Jia et al. 2019. Clinical implementation of AI technologies will require interpretable AI models. Medical Physics 47, 1 (2019), 1–4

2019
[96]

Chong Jiang, Zekun Jiang, Zitong Zhang, Hexiao Huang, et al . 2025. An explainable transformer model integrating PET and tabular data for histologic grading and prognosis of follicular lymphoma: a multi-institutional digital biopsy study. Eur. J. Nucl. Med. Mol. Imaging (2025), 1–13

2025
[97]

Hongyang Jiang, Jie Xu, Rongjie Shi, Kang Yang, Dongdong Zhang, Mengdi Gao, He Ma, and Wei Qian. 2020. A multi-label deep learning model with interpretable grad-CAM for diabetic retinopathy classification. In EMBC. 1560–1563

2020
[98]

Jiehui Jiang, Chenyang Li, et al. 2025. Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [18F] FDG PET study. European Radiology 35, 5 (2025), 2620–2633

2025
[99]

Peng-Tao Jiang, Chang-Bin Zhang, Qibin Hou, Ming-Ming Cheng, and Yunchao Wei. 2021. LayerCAM: Exploring Hierarchical Class Activation Maps for Localization. IEEE Transactions on Image Processing 30 (2021), 5875–5888. doi:10.1109/TIP.2021.3089943

2021
[100]

Weina Jin, Xiaoxiao Li, and Ghassan Hamarneh. 2022. Evaluating explainable AI on a multi-modal medical imaging task: Can existing algorithms fulfill clinical requirements?. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 11945–11953

2022
[101]

Daesung Kang, Hye Mi Gweon, Na Lae Eun, Ji Hyun Youk, Jeong-Ah Kim, and Eun Ju Son. 2021. A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis. Scientific reports 11, 1 (2021), 23925

2021
[102]

Qingbo Kang, Qicheng Lao, Yiyue Li, Zekun Jiang, Yue Qiu, Shaoting Zhang, and Kang Li. 2022. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79 (2022), 102443

2022
[103]

Andrei Kapishnikov, Tolga Bolukbasi, Fernanda Viégas, and Michael Terry. 2019. Xrai: Better attributions through regions. In ICCV. 4948–4957

2019
[104]

Dmitry Kaplun, Alexander Krasichkov, Petr Chetyrbok, Oleinikov, et al. 2021. Cancer cell profiling using image moments and neural networks with model agnostic explainability: A case study of breast cancer histopathological (BreakHis) database. Mathematics 9, 20 (2021), 2616

2021
[105]

Md Rezaul Karim, Till Döhmen, Michael Cochez, Beyan, et al. 2020. Deepcovidexplainer: explainable COVID-19 diagnosis from chest X-ray images. In 2020 IEEE international conference on bioinformatics and biomedicine (BIBM) . IEEE, 1034–1037

2020
[106]

Sofoklis Katakis, Nikolaos Barotsis, Alexandros Kakotaritis, Panagiotis Tsiganos, George Economou, Elias Panagiotopoulos, and George Panayiotakis
[107]

Navdeep Kaur and Ajay Mittal. 2022. RadioBERT: A deep learning-based system for medical report generation from chest X-ray images using contextual embeddings. Journal of biomedical informatics 135 (2022), 104220

2022
[108]

Diagnostics 13, 2 (2023), 217

Muscle cross-sectional area segmentation in transverse ultrasound images using vision transformers. Diagnostics 13, 2 (2023), 217

2023
[109]

Tarek Khater, Sam Ansari, Soliman Mahmoud, Abir Hussain, and Hissam Tawfik. 2023. Skin cancer classification using explainable artificial intelligence on pre-extracted image features. Intelligent Systems with Applications 20 (Nov. 2023), 200275. doi:10.1016/j.iswa.2023.200275

2023
[110]

Maxime Kayser, Cornelius Emde, Oana-Maria Camburu, Guy Parsons, Bartlomiej Papiez, and Thomas Lukasiewicz. 2022. Explaining chest x-ray pathologies in natural language. In International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 701–713

2022
[111]

Gi-Youn Kim, Byoung-Doo Oh, Chulho Kim, and Yu-Seop Kim. 2023. Convolutional neural network and language model-based sequential CT Image captioning for intracerebral hemorrhage. Applied Sciences 13, 17 (2023), 9665

2023
[112]

Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning . PMLR, 2668–2677

2018
[113]

Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In ICML. PMLR, 1885–1894

2017
[114]

Jieun Kim, Byeong Su Kim, Insung Choi, Zepa Yang, and Beakcheol Jang. 2024. FTT: Fourier transform based transformer for brain CT report generation. In 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) . IEEE, 617–621

2024
[115]

Shiva prasad Koyyada and Thipendra P. Singh. 2023. An explainable artificial intelligence model for identifying local indicators and detecting lung disease from chest X-ray images. Healthcare Analytics 4 (Dec. 2023), 100206. doi:10.1016/j.health.2023.100206

2023
[116]

Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020. Concept bottleneck models. In International conference on machine learning . PMLR, 5338–5348

2020
[117]

K Lakshmi, Sibi Amaran, G Subbulakshmi, S Padmini, Gyanenedra Prasad Joshi, and Woong Cho. 2025. Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images. Scientific Reports 15, 1 (2025), 690

2025
[118]

Dai Kusumoto, Takumi Akiyama, Masahiro Hashimoto, Yu Iwabuchi, Toshiomi Katsuki, Kimura, et al. 2024. A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging. Scientific reports 14, 1 (2024), 13583

2024
[119]

Minglei Li, Xiang Li, Yuchen Jiang, Jiusi Zhang, Hao Luo, and Shen Yin. 2022. Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images. Knowledge-Based Systems 252 (2022), 109278

2022
[120]

Ki-Sun Lee, Jae Young Kim, Jeon, et al. 2020. Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm. Journal of Personalized Medicine 10, 4 (Nov. 2020), 213

2020
[121]

Jean PO Lima, Roberto d’Amore, Marcos ROA Máximo, Marcus H Victor Jr, and Mônica MS Matsumoto. 2022. Evaluation of Explainable AI Methods in CNN Classifiers of COVID-19 CT Images. In Latin American Conference on Biomedical Engineering . Springer, 313–323

2022
[122]

Yi-xin Li, Feng Chen, Jiao-jiao Shi, Yu-li Huang, and Mei Wang. 2023. Convolutional neural networks for classifying cervical cancer types using histological images. Journal of Digital Imaging 36, 2 (2023), 441–449

2023
[123]

Jie Liu, Hong Lai, Jinshu Ma, and Shuchao Pang. 2022. Contennet: Quantum tensor-augmented convolutional representations for breast cancer histopathological image classification. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 1654–1657. M...

2022
[124]

Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. 74–81

2004
[125]

Xiao Liu, Weimin Li, Shang Miao, Fangyu Liu, Ke Han, and Tsigabu T Bezabih. 2024. HAMMF: hierarchical attention-based multi-task and multi-modaldi fusion model for computer-aided diagnosis of Alzheimer’s disease. Computers in Biology and Medicine 176 (2024), 108564

2024
[126]

Liangliang Liu, Ying Wang, Pei Zhang, Hongbo Qiao, Tong Sun, Hui Zhang, Xue Xu, and Hongcai Shang. 2023. Collaborative transfer network for multi-classification of breast cancer histopathological images. IEEE Journal of Biomedical and Health Informatics 28, 1 (2023), 110–121

2023
[127]

Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. NeurIPS 30 (2017)

2017
[128]

Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun, Malik, et al . 2022. ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions. Computer Methods and Programs in Biomedicine 215 (March 2022), 106620

2022
[129]

Huong Hoang Luong, Hai Thanh Nguyen, and Nguyen Thai-Nghe. 2024. Toward Supporting Breast Cancer Diagnosis Based on Captioning Mammogram and Ultrasound Images. In International Conference on Intelligent Systems and Data Science . Springer, 71–85

2024
[130]

Yaozhong Luo, Qinghua Huang, and Xuelong Li. 2022. Segmentation information with attention integration for classification of breast tumor in ultrasound image. Pattern Recognition 124 (2022), 108427

2022
[131]

Bruno Magalhães, João Pedrosa, Francesco Renna, Hugo Paredes, and Vitor Filipe. 2024. Image Captioning for Coronary Artery Disease Diagnosis. In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 5302–5308

2024
[132]

Daniel C Macedo et al. 2022. Evaluating Interpretability in Deep Learning using Breast Cancer Histopathological Images. In 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) , Vol. 1. IEEE, 276–281

2022
[133]

Tanvir Mahmud et al. 2020. CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Computers in Biology and Medicine 122 (July 2020), 103869

2020
[134]

SM Mahim, Md Shahin Ali, Md Olid Hasan, and others Nafi. 2024. Unlocking the Potential of XAI for Improved Alzheimer’s Disease Detection and Classification Using a ViT-GRU Model. IEEE Access (2024)

2024
[135]

Saurabh Mandloi, Mohd Zuber, and Rajeev Kumar Gupta. 2024. An explainable brain tumor detection and classification model using deep learning and layer-wise relevance propagation. Multimedia Tools and Applications 83, 11 (2024), 33753–33783

2024
[136]

Tanjim Mahmud, Koushick Barua, Sultana Umme Habiba, Nahed Sharmen, Mohammad Shahadat Hossain, and Karl Andersson. 2024. An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning. Diagnostics 14, 3 (2024), 345

2024
[137]

James Mayanja, Enoch Hall Asanda, Joshua Mwesigwa, Pius Tumwebaze, and Ggaliwango Marvin. 2023. Explainable Artificial Intelligence and Deep Transfer Learning for Skin Disease Diagnosis. In International Conference on Image Processing and Capsule Networks . Springer, 711–724

2023
[138]

Van T Manh, Jianqiao Zhou, Xiaohong Jia, Zehui Lin, Wenwen Xu, et al. 2022. Multi-attribute attention network for interpretable diagnosis of thyroid nodules in ultrasound images. IEEE transactions on ultrasonics, ferroelectrics, and frequency control 69, 9 (2022), 2611–2620

2022
[139]

Wan Sabrina Mayzura, Riyanarto Sarno, Nur Setiawan Suroto, Muhammad Ibadurrahman Arrasyid Supriyanto, and Gerry Sihaj. 2025. Automatic Interpretation of Brain Medical Images Using Hierarchical Classification and Image Captioning Model. IEEE Access (2025)

2025
[140]

Hilda Mayrose, Niranjana Sampathila, G Muralidhar Bairy, et al . 2024. An explainable artificial intelligence integrated system for automatic detection of dengue from images of blood smears using transfer learning. IEEE Access 12 (2024), 41750–41762

2024
[141]

Francesco Mercaldo, Maria Paola Belfiore, Alfonso Reginelli, Luca Brunese, and Antonella Santone. 2023. Coronavirus covid-19 detection by means of explainable deep learning. Scientific Reports 13, 1 (Jan. 2023). doi:10.1038/s41598-023-27697-y

2023 doi
[142]

Leland McInnes, John Healy, and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)

2018 arXiv
[143]

Jamie Morris, Zehao Liu, Huizhi Liang, Sidhartha Nagala, and Xia Hong. 2023. ThyExp: an explainable AI-assisted decision making toolkit for thyroid nodule diagnosis based on ultra-sound images. In ACM International Conference on Information and Knowledge Management . 5371–5375

2023
[144]

Aqib Nazir Mir et al . 2025. Enhancing histopathological image analysis: An explainable vision transformer approach with comprehensive interpretation methods and evaluation of explanation quality. Engineering Applications of Artificial Intelligence 149 (2025), 110519

2025
[145]

Satya M Muddamsetty, NS Jahromi Mohammad, and Thomas B Moeslund. 2020. Sidu: Similarity difference and uniqueness method for explainable ai. In 2020 IEEE International Conference on Image Processing (ICIP) . IEEE, 3269–3273

2020
[146]

Mezbah Uddin, Jungpil Shin, Susan Khadka, and M

Krishna Mridha, Md. Mezbah Uddin, Jungpil Shin, Susan Khadka, and M. F. Mridha. 2023. An Interpretable Skin Cancer Classification Using Optimized Convolutional Neural Network for a Smart Healthcare System. IEEE Access 11 (2023), 41003–41018. doi:10.1109/access.2023.3269694

2023
[147]

Doniyorjon Mukhtorov, Madinakhon Rakhmonova, Shakhnoza Muksimova, and Young-Im Cho. 2023. Endoscopic image classification based on explainable deep learning. Sensors 23, 6 (2023), 3176

2023
[148]

Mohammed Bany Muhammad and Mohammed Yeasin. 2020. Eigen-cam: Class activation map using principal components. In 2020 international joint conference on neural networks (IJCNN) . IEEE, 1–7

2020
[149]

Nafisah and Ghulam Muhammad

Saad I. Nafisah and Ghulam Muhammad. 2022. Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence. Neural Computing and Applications 36, 1 (April 2022), 111–131. doi:10.1007/s00521-022-07258-6

2022 doi
[150]

Philip Müller, Georgios Kaissis, and Daniel Rueckert. 2024. ChEX: Interactive Localization and Region Description in Chest X-rays. In European Conference on Computer Vision . Springer, 92–111

2024
[151]

Parth Natekar, Avinash Kori, and Ganapathy Krishnamurthi. 2020. Demystifying brain tumor segmentation networks: interpretability and uncertainty analysis. Frontiers in computational neuroscience 14 (2020), 6. Manuscript submitted to ACM 32 Getamesay et al

2020
[152]

Rakshit Naidu, Ankita Ghosh, Yash Maurya, Shamanth R Nayak K, and Soumya Snigdha Kundu. 2020. IS-CAM: Integrated Score-CAM for axiomatic-based explanations. arXiv:2010.03023 [cs.CV]

2020 arXiv
[153]

Dickson, and Muhammad Usman Akram

Sajid Nazir, Diane M. Dickson, and Muhammad Usman Akram. 2023. Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks. Computers in Biology and Medicine 156 (April 2023), 106668. doi:10.1016/j.compbiomed.2023.106668

2023
[154]

Mahmood Nazari et al. 2022. Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes. Eur. J. Nucl. Med. Mol. Imaging (2022), 1–11

2022
[155]

Yuxiang Nie, Sunan He, Yequan Bie, Yihui Wang, Zhixuan Chen, Shu Yang, and Hao Chen. 2025. ConceptCLIP: Towards Trustworthy Medical AI via Concept-Enhanced Contrastive Langauge-Image Pre-training. arXiv preprint arXiv:2501.15579 (2025)

2025 arXiv
[156]

Wandile Nhlapho, Marcellin Atemkeng, Yusuf Brima, and Jean-Claude Ndogmo. 2024. Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images. Information 15, 4 (2024), 182

2024
[157]

K Niranjan, S Shankar Kumar, S Vedanth, and S Chitrakala. 2023. An explainable ai driven decision support system for covid-19 diagnosis using fused classification and segmentation. Procedia computer science 218 (2023), 1915–1925

2023
[158]

Natasha Nigar, Muhammad Umar, Muhammad Kashif Shahzad, Shahid Islam, and Douhadji Abalo. 2022. A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification. IEEE Access 10 (2022), 113715–113725. doi:10.1109/access.2022.3217217

2022
[159]

Fabrizio Nunnari, Md Abdul Kadir, and Daniel Sonntag. 2021. On the overlap between grad-cam saliency maps and explainable visual features in skin cancer images. In International cross-domain conference for machine learning and knowledge extraction . Springer, 241–253

2021
[160]

Muhammad Nouman Noor, Muhammad Nazir, Sajid Ali Khan, Imran Ashraf, and Oh-Young Song. 2023. Localization and Classification of Gastrointestinal Tract Disorders Using Explainable AI from Endoscopic Images. Applied Sciences 13, 15 (Aug. 2023), 9031. doi:10.3390/app13159031

2023 doi
[161]

Joe Huei Ong, Kam Meng Goh, and Li Li Lim. 2021. Comparative analysis of explainable artificial intelligence for COVID-19 diagnosis on CXR image. In 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) . IEEE, 185–190

2021
[162]

Daniel Omeiza, Skyler Speakman, Celia Cintas, and Komminist Weldermariam. 2019. Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models. arXiv:1908.01224 [cs.CV]

2019 arXiv
[163]

Ting Pang, Jeannie Hsiu Ding Wong, Wei Lin Ng, Chee Seng Chan, et al. 2024. Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification. Physics in Medicine & Biology 69, 6 (2024), 065006

2024
[164]

Yuka Otaki, Ananya Singh, Paul Kavanagh, Robert JH Miller, Tejas Parekh, Balaji K Tamarappoo, Tali Sharir, Andrew J Einstein, et al. 2022. Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease. Cardiovascular Imaging 15, 6...

2022
[165]

Nikolaos I Papandrianos, Anna Feleki, Serafeim Moustakidis, Elpiniki I Papageorgiou, et al. 2022. An explainable classification method of SPECT myocardial perfusion images in nuclear cardiology using deep learning and grad-CAM. Applied Sciences 12, 15 (2022), 7592

2022
[166]

Winnie Pang, Xueyi Ke, Satoshi Tsutsui, and Bihan Wen. 2024. Integrating Clinical Knowledge into Concept Bottleneck Models. In International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 243–253

2024
[167]

Cristiano Patrício et al. 2023. Explainable deep learning methods in medical image classification: A survey. Comput. Surveys 56, 4 (2023), 1–41

2023
[168]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. InProceedings of the 40th annual meeting of the Association for Computational Linguistics . 311–318

2002
[169]

Cristiano Patrício, Luis F Teixeira, and João C Neves. 2024. Towards concept-based interpretability of skin lesion diagnosis using vision-language models. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI) . IEEE, 1–5

2024
[170]

Cristiano Patrício, João C Neves, and Luis F Teixeira. 2023. Coherent concept-based explanations in medical image and its application to skin lesion diagnosis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3799–3808

2023
[171]

Said Pertuz, David Ortega, Érika Suarez, William Cancino, Gerson Africano, Irina Rinta-Kiikka, Otso Arponen, Sara Paris, and Alfonso Lozano
[172]

Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schinina, Simone Palazzo, Federica Proietto Salanitri, et al. 2021. An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans. Artificial Intelligence in Medicine 118 (Aug. ...

2021
[173]

Vitali Petsiuk et al. 2018. Rise: Randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421 (2018)

2018 arXiv
[174]

Scientific Reports 13, 1 (2023), 20545

Saliency of breast lesions in breast cancer detection using artificial intelligence. Scientific Reports 13, 1 (2023), 20545

2023
[175]

Jyothi Peta and Srinivas Koppu. 2024. Explainable Soft Attentive EfficientNet for breast cancer classification in histopathological images.Biomedical Signal Processing and Control 90 (2024), 105828

2024
[176]

Vidhi Pitroda, Mostafa M Fouda, and Zubair Md Fadlullah. 2021. An explainable AI model for interpretable lung disease classification. In 2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) . IEEE, 98–103

2021
[177]

Trong Thang Pham, Jacob Brecheisen, Anh Nguyen, Hien Nguyen, and Ngan Le. 2024. I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses. In IEEE/CVF winter conference on applications of computer vision . 7850–7859

2024
[178]

Trong Thang Pham, Ngoc-Vuong Ho, Nhat-Tan Bui, Thinh Phan, Patel Brijesh, et al . 2024. FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation. In Asian Conference on Computer Vision . 941–958

2024
[179]

Bai Qing, Sun Zhanquan, Wang Kang, Wang Chaoli, Cheng Shuqun, and Zhang Jiawei. 2024. MPSA: Multi-Position Supervised Soft Attention-based convolutional neural network for histopathological image classification. Expert Systems with Applications 253 (2024), 124336

2024
[180]

Clément Playout, Renaud Duval, Marie Carole Boucher, and Farida Cheriet. 2022. Focused attention in transformers for interpretable classification of retinal images. Medical Image Analysis 82 (2022), 102608

2022
[181]

Francesco Prinzi, Marco Insalaco, Alessia Orlando, Salvatore Gaglio, and Salvatore Vitabile. 2024. A YOLO-based model for breast cancer detection in mammograms. Cognitive Computation 16, 1 (2024), 107–120

2024
[182]

Tawsifur Rahman, Amith Khandakar, Muhammad Abdul Kadir, et al. 2020. Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization. IEEE Access 8 (2020), 191586–191601

2020
[183]

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning . PMLR, 8748–8763. Manuscript submitted to ACM Explainable Artificial Intelligence in ...

2021
[184]

Kaushik Raghavan. 2023. Attention guided grad-CAM: an improved explainable artificial intelligence model for infrared breast cancer detection. Multimedia Tools and Applications (2023), 1–28

2023
[185]

Md Tanzim Reza, Farzad Ahmed, Shihab Sharar, and Annajiat Alim Rasel. 2021. Interpretable retinal disease classification from oct images using deep neural network and explainable ai. In ICECIT. IEEE, 1–4

2021
[186]

N Shobha Rani, CH Nachappa, Arun Sri Krishna, and BJ Bipin Nair. 2022. Multi Disease Diagnosis Model for Chest X-ray Images with Explainable AI–Grad-Cam Feature Map Visualization. In 2022 International Conference on Futuristic Technologies (INCOFT) . IEEE, 1–5

2022
[187]

Rehan Raza, Fatima Zulfiqar, Muhammad Owais Khan, Muhammad Arif, Atif Alvi, Muhammad Aksam Iftikhar, and Tanvir Alam. 2023. Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images. Engineering Applications of Artificial Intelligence 126 (2023), 106902

2023
[188]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018. Anchors: High-precision model-agnostic explanations. In Proceedings of the AAAI conference on artificial intelligence , Vol. 32

2018
[189]

Alireza Rezazadeh, Yasamin Jafarian, and Ali Kord. 2022. Explainable ensemble machine learning for breast cancer diagnosis based on ultrasound image texture features. Forecasting 4, 1 (2022), 262–274

2022
[190]

Why should i trust you?

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. " Why should i trust you?" Explaining the predictions of any classifier. InProceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . 1135–1144

2016
[191]

Woodruff, Avishek Chatterjee, and Philippe Lambin

Zohaib Salahuddin, Henry C. Woodruff, Avishek Chatterjee, and Philippe Lambin. 2022. Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Computers in Biology and Medicine 140 (Jan. 2022), 105111. doi:10.1016/j.compbiomed.2021.105111

2022
[194]

Daniel Sauter, Georg Lodde, Felix Nensa, Dirk Schadendorf, Elisabeth Livingstone, and Markus Kukuk. 2022. Validating automatic concept-based explanations for AI-based digital histopathology. Sensors 22, 14 (2022), 5346

2022
[195]

Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller. 2016. Evaluating the visualization of what a Deep Neural Network has learned. IEEE transactions on neural networks and learning systems 28, 11 (2016), 2660–2673

2016
[196]

Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller. 2016. Evaluating the visualization of what a deep neural network has learned. IEEE transactions on neural networks and learning systems 28, 11 (2016), 2660–2673

2016
[197]

Ruey-Kai Sheu and Mayuresh Sunil Pardeshi. 2022. A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System. Sensors 22, 20 (2022), 8068

2022
[198]

Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In ICCV. 618–626

2017
[199]

Ajay Sharma and Pramod Kumar Mishra. 2022. Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images. Pattern Recognition 131 (Nov. 2022), 108826. doi:10.1016/j.patcog.2022.108826

2022
[200]

Mohammad Shorfuzzaman. 2021. An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection. Multimedia Systems 28, 4 (April 2021), 1309–1323. doi:10.1007/s00530-021-00787-5

2021 doi
[201]

Ruey-Kai Sheu, Mayuresh Sunil Pardeshi, Kai-Chih Pai, Lun-Chi Chen, Chieh-Liang Wu, and Wei-Cheng Chen. 2023. Interpretable Classification of Pneumonia Infection Using eXplainable AI (XAI-ICP). IEEE Access 11 (2023), 28896–28919. doi:10.1109/access.2023.3255403

2023
[202]

Hyungseob Shin, Ji Eun Park, Yohan Jun, Taejoon Eo, Jeongryong Lee, et al. 2023. Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI. European Radiology 33, 8 (2023), 5859–5870

2023
[203]

Amitojdeep Singh, Sourya Sengupta, and Vasudevan Lakshminarayanan. 2020. Explainable Deep Learning Models in Medical Image Analysis. Journal of Imaging 6, 6 (June 2020), 52. doi:10.3390/jimaging6060052

2020 doi
[204]

Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning important features through propagating activation differences. In International conference on machine learning . PMlR, 3145–3153

2017
[205]

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)

2013 arXiv
[206]

Soni Singh, Pankaj K Jain, Neeraj Sharma, Mausumi Pohit, and Sudipta Roy. 2024. Atherosclerotic plaque classification in carotid ultrasound images using machine learning and explainable deep learning. Intelligent Medicine 4, 2 (2024), 83–95

2024
[207]

Dilbag Singh, Manjit Kaur, Jazem Mutared Alanazi, Ahmad Ali AlZubi, and Heung-No Lee. 2022. Efficient evolving deep ensemble medical image captioning network. IEEE Journal of Biomedical and Health Informatics 27, 2 (2022), 1016–1025

2022
[208]

Rajeev Kumar Singh, Rohan Pandey, and Rishie Nandhan Babu. 2021. COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays. Neural Computing and Applications 33, 14 (Jan. 2021), 8871–8892. doi:10.1007/s00521-020-05636-6

2021 doi
[209]

Boyue Song et al. 2024. Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer’s disease classification based on gradient-weighted class activation mapping. Plos one 19, 5 (2024), e0303278

2024
[210]

Daniel Smilkov et al. 2017. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)

2017 arXiv
[211]

Eduardo Soares, Plamen Angelov, and Ziyang Zhang. 2024. An explainable approach to deep learning from CT-scans for covid identification. Evolving Systems 15, 6 (2024), 2159–2168. Manuscript submitted to ACM 34 Getamesay et al

2024
[212]

Fabian Stieler, Fabian Rabe, and Bernhard Bauer. 2021. Towards domain-specific explainable AI: model interpretation of a skin image classifier using a human approach. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1802–1809

2021
[213]

Jost Tobias Springenberg et al. 2014. Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806 (2014)

2014 arXiv
[214]

Suraj Srinivas and François Fleuret. 2019. Full-Gradient Representation for Neural Network Visualization. NeurIPS 32 (2019)

2019
[215]

Taly Sundararajan, Mukund et al. 2017. Axiomatic attribution for deep networks. In International conference on machine learning . 3319–3328

2017
[216]

S Suganyadevi et al. 2022. A review on deep learning in medical image analysis. Int. J. Multimed. Inf. Retr. 11, 1 (2022), 19–38

2022
[217]

Yong Joon Suh, Jaewon Jung, and Bum-Joo Cho. 2020. Automated breast cancer detection in digital mammograms of various densities via deep learning. Journal of personalized medicine 10, 4 (2020), 211

2020
[218]

Toshiyuki Terunuma, Takeji Sakae, Yachao Hu, Hideyuki Takei, et al. 2023. Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy. Medical Physics 50, 1 (2023), 480–494

2023
[219]

R, Vinoth Kumar V, and Suresh Guluwadi

Mahesh T. R, Vinoth Kumar V, and Suresh Guluwadi. 2024. Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50. BMC medical imaging 24, 1 (2024), 107

2024
[220]

Yuhao Tang, Haichen Yang, Liyan Zhang, and Ye Yuan. 2024. Work like a doctor: Unifying scan localizer and dynamic generator for automated computed tomography report generation. Expert Systems with Applications 237 (2024), 121442

2024
[221]

Satoshi Tsutsui, Winnie Pang, and Bihan Wen. 2023. Wbcatt: A white blood cell dataset annotated with detailed morphological attributes.Advances in Neural Information Processing Systems 36 (2023), 50796–50824

2023
[222]

Mahima Thakur, Harisudha Kuresan, Samiappan Dhanalakshmi, Khin Wee Lai, and Xiang Wu. 2022. Soft attention based DenseNet model for Parkinson’s disease classification using SPECT images. Frontiers in Aging Neuroscience 14 (2022), 908143

2022
[223]

Jayaraman J Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, and Brian Spears. 2020. Designing accurate emulators for scientific processes using calibration-driven deep models. Nature Communications 11, 1 (2020), 5622

2020
[224]

Naeem Ullah, Muhammad Hassan, Javed Ali Khan, et al. 2024. Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images. International Journal of Imaging Systems and Technology 34, 1 (2024), e23012

2024
[225]

Kazuki Uehara, Masahiro Murakawa, Hirokazu Nosato, and Hidenori Sakanashi. 2019. Prototype-based interpretation of pathological image analysis by convolutional neural networks. In Asian Conference on Pattern Recognition . Springer, 640–652

2019
[226]

Ukwuoma, Zhiguang Qin, Md Belal Bin Heyat, Faijan Akhtar, Olusola Bamisile, et al

Chiagoziem C. Ukwuoma, Zhiguang Qin, Md Belal Bin Heyat, Faijan Akhtar, Olusola Bamisile, et al . 2023. A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. Journal of Advanced Research 48 (June 2023), 191–211

2023
[227]

Arnaud Van Looveren and Janis Klaise. 2021. Interpretable counterfactual explanations guided by prototypes. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 650–665

2021
[228]

van der Velden, Hugo J

Bas H.M. van der Velden, Hugo J. Kuijf, Kenneth G.A. Gilhuijs, and Max A. Viergever. 2022. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis 79 (July 2022), 102470. doi:10.1016/j.media.2022.102470

2022
[229]

Michael Van Lent, William Fisher, and Michael Mancuso. 2004. An explainable artificial intelligence system for small-unit tactical behavior. In Proceedings of the national conference on artificial intelligence . Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 199...

2004
[230]

Lawrence Zitnick, and Devi Parikh

Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh. 2015. CIDEr: Consensus-based Image Description Evaluation. In CVPR. 4566–4575

2015
[231]

SA Varaprasad and Tripti Goel. 2025. Exploring the significance of the frontal lobe for diagnosis of schizophrenia using explainable artificial intelligence and group level analysis. Psychiatry Research: Neuroimaging 349 (2025), 111969

2025
[232]

Mariana Vasquez, Suhev Shakya, Ian Wang, Jacob Furst, Roselyne Tchoua, and Daniela Raicu. 2022. Interactive deep learning for explainable retinal disease classification. In Medical Imaging 2022: Image Processing , Vol. 12032. SPIE, 148–155

2022
[233]

Egor N Volkov and Aleksej N Averkin. 2023. Possibilities of explainable artificial intelligence for glaucoma detection using the lime method as an example. In 2023 XXVI International Conference on Soft Computing and Measurements (SCM) . IEEE, 130–133

2023
[234]

Cleverson Marques Vieira, Marcus Vinícius De Castro Oliveira, et al. 2023. Applied Explainable Artificial Intelligence (XAI) in the classification of retinal images for support in the diagnosis of Glaucoma. In Brazilian Symposium on Multimedia and the Web . 82–90

2023
[235]

Egor N Volkov and Aleksej N Averkin. 2023. Gradient-based explainable artificial intelligence methods for eye disease classification. In 2023 IV International Conference on Neural Networks and Neurotechnologies (NeuroNT) . IEEE, 6–9

2023
[236]

Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, and Jimeng Sun. 2022. Medclip: Contrastive learning from unpaired medical images and text. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. , Vol. 2022. 3876

2022
[237]

Haofan Wang et al. 2020. SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization. arXiv:2006.14255 [cs.CV]

2020 arXiv
[238]

H. Wang, Z. Wang, M. Du, F. Yang, Z. Zhang, S. Ding, P. Mardziel, and X. Hu. 2020. Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks. In IEEE/CVF Conference on CVPR Workshops. IEEE Computer Society, Los Alamitos, CA, USA, 111–119

2020
[239]

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning . PMLR, 2048–2057. Manuscrip...

2015
[240]

Yuanyuan Wei, Roger Tam, and Xiaoying Tang. 2024. Mprotonet: A case-based interpretable model for brain tumor classification with 3d multi-parametric magnetic resonance imaging. In Medical Imaging with Deep Learning . PMLR, 1798–1812

2024
[241]

Yifan Wu, Yang Liu, Yue Yang, Michael S Yao, Wenli Yang, Xuehui Shi, Lihong Yang, Dongjun Li, Yueming Liu, Shiyi Yin, et al. 2025. A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data. Nature Communications 16, 1 (2025), 3504

2025
[242]

Kaiwen Yang, Aiga Suzuki, Jiaxing Ye, Hirokazu Nosato, Ayumi Izumori, and Hidenori Sakanashi. 2022. CTG-Net: Cross-task guided network for breast ultrasound diagnosis. PloS one 17, 8 (2022), e0271106

2022
[243]

Dangui Yang et al. 2025. A Multi-Scale Interpretability-Based PET-CT Tumor Segmentation Method. Mathematics 13, 7 (2025), 1139

2025
[244]

Fan Yang et al. 2025. AI in SPECT Imaging: Opportunities and Challenges. In Seminars in Nuclear Medicine . Elsevier

2025
[245]

Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang , Tomas Pfister, et al. 2020. On completeness-aware concept-based explanations in deep neural networks. Advances in neural information processing systems 33 (2020), 20554–20565

2020
[246]

Mei Yang, Zhiying Xie, Zhaoxia Wang, Yun Yuan, and Jue Zhang. 2022. Su-micl: severity-guided multiple instance curriculum learning for histopathology image interpretable classification. IEEE Transactions on Medical Imaging 41, 12 (2022), 3533–3543

2022
[247]

Qinghao Ye, Jun Xia, and Guang Yang. 2021. Explainable AI for COVID-19 CT classifiers: an initial comparison study. In2021 IEEE 34th international symposium on computer-based medical systems (CBMS) . IEEE, 521–526

2021
[248]

Hao-lin Yin et al. 2022. A magnetic resonance angiography-based study comparing machine learning and clinical evaluation: screening intracranial regions associated with the hemorrhagic stroke of adult moyamoya disease. Journal of Stroke and Cerebrovascular Diseases 31, 4 (2022...

2022
[249]

Sena Busra Yengec-Tasdemir, Zafer Aydin, Ebru Akay, Serkan Dogan, and Bulent Yilmaz. 2023. Improved classification of colorectal polyps on histopathological images with ensemble learning and stain normalization. Computer Methods and Programs in Biomedicine 232 (2023), 107441

2023
[250]

Muhammed Yildirim. 2023. Image visualization and classification using hydatid cyst images with an explainable hybrid model. Applied Sciences 13, 17 (2023), 9926

2023
[251]

Lu Yu et al. 2022. A novel explainable neural network for Alzheimer’s disease diagnosis. Pattern Recognition 131 (2022), 108876

2022
[252]

Ming Ping Yong, Yan Chai Hum, Khin Wee Lai, Ying Loong Lee, Choon-Hian Goh, Wun-She Yap, and Yee Kai Tee. 2023. Histopathological cancer detection using intra-domain transfer learning and ensemble learning. IEEE Access 12 (2023), 1434–1457

2023
[254]

Matthew D Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In ECCV. Springer, 818–833

2014
[255]

Mei Yu, Ming Han, Xuewei Li, Xi Wei, Han Jiang, Huiling Chen, and Ruiguo Yu. 2022. Adaptive soft erasure with edge self-attention for weakly supervised semantic segmentation: thyroid ultrasound image case study. Computers in Biology and Medicine 144 (2022), 105347

2022
[256]

Mert Yuksekgonul, Maggie Wang, and James Zou. 2022. Post-hoc concept bottleneck models. arXiv preprint arXiv:2205.15480 (2022)

2022 arXiv
[257]

Jianming Zhang et al. 2018. Top-down neural attention by excitation backprop. International Journal of Computer Vision 126, 10 (2018), 1084–1102

2018
[258]

Ramy A Zeineldin, Mohamed E Karar, Ziad Elshaer, · Jan Coburger, Christian R Wirtz, et al. 2022. Explainability of deep neural networks for MRI analysis of brain tumors. International journal of computer assisted radiology and surgery 17, 9 (2022), 1673–1683

2022
[259]

Hao Zhang, Genji Yuan, Ziyue Zhang, Xiang Guo, Ruixiang Xu, Tongshuai Xu, et al. 2024. A multi-scene deep learning model for automated segmentation of acute vertebral compression fractures from radiographs: a multicenter cohort study. Insights into Imaging 15, 1 (2024), 1–11

2024
[260]

Yingying Zhang, Haogang Zhu, Jian Cheng, Jingyi Wang, Xiaoyan Gu, et al. 2023. Improving the Quality of Fetal Heart Ultrasound Imaging With Multihead Enhanced Self-Attention and Contrastive Learning. IEEE Journal of Biomedical and Health Informatics 27, 11 (Nov. 2023), 5518–5529

2023
[261]

Jianpeng Zhang et al. 2019. Attention Residual Learning for Skin Lesion Classification. IEEE TMI 38, 9 (Sept. 2019), 2092–2103

2019
[262]

Sheng Zhang, Yanbo Xu, Naoto Usuyama, Hanwen Xu, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, et al. 2023. BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv preprint arXiv:2303.00915 (2023)

2023 arXiv
[263]

Xiongjun Zhao, Zhengyu Liu, Fen Liu, Guanting Li, Yutao Dou, and Shaoliang Peng. 2024. Report-concept textual-prompt learning for enhancing x-ray diagnosis. In Proceedings of the 32nd ACM International Conference on Multimedia . 2184–2193

2024
[264]

Zizhao Zhang et al. 2017. Mdnet: A semantically and visually interpretable medical image diagnosis network. In CVPR. 6428–6436

2017
[265]

Jianfeng Zhao and Shuo Li. 2024. Evidence modeling for reliability learning and interpretable decision-making under multi-modality medical image segmentation. Computerized Medical Imaging and Graphics 116 (2024), 102422

2024
[266]

Houliang Zhou, Lifang He, Yu Zhang, Li Shen, and Brian Chen. 2022. Interpretable graph convolutional network of multi-modality brain imaging for alzheimer’s disease diagnosis. In IEEE International Symposium on Biomedical Imaging . 1–5

2022
[267]

Hangbin Zheng, Zhixia Dong, Tianyuan Liu, et al. 2024. Enhancing gastrointestinal submucosal tumor recognition in endoscopic ultrasonography: A novel multi-attribute guided contextual attention network. Expert Systems with Applications 242 (2024), 122725

2024
[268]

B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. 2016. Learning Deep Features for Discriminative Localization. In IEEE Conference on CVPR. IEEE Computer Society, Los Alamitos, CA, USA, 2921–2929

2016
[269]

Yanming Zhu and Erik Meijering. 2020. Neural architecture search for microscopy cell segmentation. In Machine Learning in Medical Imaging: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 11 . Springer, 542–551

2020
[270]

Ziyu Zhou et al. 2024. An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis. arXiv:2404.00144

2024 arXiv
[271]

Yanming Zhu et al. 2024. AC-UNet: Adaptive Connection UNet for White Matter Tract Segmentation Through Neural Architecture Search. In IEEE International Symposium on Biomedical Imaging . 1–5

2024
[272]

Lin Zou, Han Leong Goh, Charlene Jin Yee Liew, Jessica Lishan Quah, et al. 2023. Ensemble Image Explainable AI (XAI) Algorithm for Severe Community-Acquired Pneumonia and COVID-19 Respiratory Infections. IEEE Transactions on Artificial Intelligence 4, 2 (April 2023), 242–254. ...

2023
[273]

Yanming Zhu, Xuefei Yin, and Erik Meijering. 2022. A compound loss function with shape aware weight map for microscopy cell segmentation. IEEE Transactions on Medical Imaging 42, 5 (2022), 1278–1288

2022
[274]

Alsuhibany, Sajjad Shaukat Jamal, Muhammad Zulfiqar Ali, and Jawad Ahmad

Muhammad Zia Ur Rehman, Fawad Ahmed, Suliman A. Alsuhibany, Sajjad Shaukat Jamal, Muhammad Zulfiqar Ali, and Jawad Ahmad. 2022. Classification of Skin Cancer Lesions Using Explainable Deep Learning. Sensors 22, 18 (Sept. 2022), 6915. doi:10.3390/s22186915

2022 doi
[2023]

Diagnostic Pathology 18, 1 (2023)

Explainable convolutional neural networks for assessing head and neck cancer histopathology. Diagnostic Pathology 18, 1 (2023)

2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [253]

Alan S. Young. 2022. AI in healthcare startups and special challenges. Intelligence-Based Medicine 6 (2022), 100050. doi:10.1016/j.ibmed.2022.100050

arXiv 2022

[2] [192]

R Ricciardi, G Mettivier, M Staffa, A Sarno, G Acampora, S Minelli, A Santoro, E Antignani, A Orientale, IAM Pilotti, et al. 2021. A deep learning classifier for digital breast tomosynthesis. Physica Medica 83 (2021), 184–193

2021

[3] [193]

Isabel Rio-Torto, Jaime S Cardoso, and Luis Filipe Teixeira. 2024. Parameter-Efficient Generation of Natural Language Explanations for Chest X-ray Classification. In Medical Imaging with Deep Learning

2024

[4] [1]

Samira Abnar and Willem Zuidema. 2020. Quantifying Attention Flow in Transformers. In Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 4190–4197

2020

[5] [2]

Md Faysal Ahamed, Md Nahiduzzaman, Md Rabiul Islam, Mansura Naznine, et al. 2024. Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AI. Expert Systems with Applications 256 (2024), 124908

2024

[6] [3]

Naveed Ahmad, Jamal Hussain Shah, Muhammad Attique Khan, Jamel Baili, Ghulam Jillani Ansari, Usman Tariq, et al. 2023. A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI. Frontiers in Oncology 13 (June 2023). Manuscript submitted to ACM Explainable Artificial Intelligence in Biomedical Im...

2023

[7] [4]

Shamim Ahmed, Sm Nuruzzaman Nobel, and Oli Ullah. 2023. An effective deep cnn model for multiclass brain tumor detection using mri images and shap explainability. In 2023 International Conference on Electrical, Computer and Communication Engineering . IEEE, 1–6

2023

[8] [5]

Sarder Tanvir Ahmed, Shomtirtha Barua, Md Fahim-Ul-Islam, and Amitabha Chakrabarty. 2024. CoAtNet-Lite: Advancing Mammogram Mass Detection Through Lightweight CNN-Transformer Fusion with Attention Mapping. In International Conference on EEICT . IEEE, 143–148

2024