arxiv: 2604.10188 · v1 · submitted 2026-04-11 · 💻 cs.CV

Recognition: unknown

Radiology Report Generation for Low-Quality X-Ray Images

Hongze Zhu , Chen Hu , Jiaxuan Jiang , Hong Liu , Yawen Huang , Ming Hu , Tianyu Wang , Zhijian Wu

show 1 more author

Yefeng Zheng

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:03 UTC · model grok-4.3

classification 💻 cs.CV

keywords radiology report generationlow-quality X-ray imagesvision-language modelsbi-level optimizationgradient consistencyrobustness to image degradationMIMIC-CXR benchmark

0 comments

The pith

A dual-loop training strategy with bi-level optimization and gradient consistency lets radiology report models maintain accuracy on low-quality X-ray images.

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

Vision-language models for automatic radiology report generation currently assume clean, high-resolution X-ray inputs and lose effectiveness when real-world scans contain noise or artifacts. The paper creates a dedicated Low-quality Radiology Report Generation benchmark by automatically identifying degraded samples from the MIMIC-CXR collection. It then introduces a dual-loop training process that applies bi-level optimization to force the model to produce the same learning updates from both high-quality and low-quality versions of the same image. This alignment is intended to extract diagnostic features that remain useful regardless of input quality. If the approach works, automated reporting systems could operate reliably in everyday clinical conditions where perfect image quality is not always available.

Core claim

The authors claim that their Dual-loop Training Strategy, which uses bi-level optimization to enforce gradient consistency across high- and low-quality image regimes, produces quality-agnostic diagnostic features and thereby reduces the performance drop that standard models suffer when generating reports from degraded X-ray images.

What carries the argument

The Dual-loop Training Strategy that applies bi-level optimization to align gradient directions between quality variants of the same image, forcing the model to learn features independent of image quality.

If this is right

Existing VLM-based report generators can adopt the dual-loop procedure without altering their core architecture and still gain robustness.
Performance degradation on low-quality inputs is reduced while high-quality performance remains intact.
The LRRG benchmark supplies a standardized test bed for measuring how well any future method handles real-world image quality variation.
The method directly targets the distribution shift caused by quality deterioration rather than relying on image enhancement as a separate step.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same gradient-alignment idea could be applied to other medical imaging modalities such as CT or ultrasound where input quality also varies.
If the learned features truly ignore quality, the approach might lower the required resolution or dose in some imaging protocols without losing diagnostic utility.
Future tests could check whether the method still works when low-quality images come from different scanners or hospitals than the training data.

Load-bearing premise

That forcing the model to follow the same gradient direction on high-quality and low-quality versions of an image will keep all clinically important diagnostic signals without discarding details that only appear in clearer scans.

What would settle it

Running the reported experiments on the LRRG benchmark and finding that the dual-loop model shows no measurable gain in report quality metrics on low-quality test cases compared with ordinary fine-tuning, while high-quality performance stays comparable.

Figures

Figures reproduced from arXiv: 2604.10188 by Chen Hu, Hong Liu, Hongze Zhu, Jiaxuan Jiang, Ming Hu, Tianyu Wang, Yawen Huang, Yefeng Zheng, Zhijian Wu.

**Figure 2.** Figure 2: Retake pairs illustrate acquisition-related quality shift. A low-quality pre-retake CXR can degrade report [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of our quality assessment agent. We first extract no-reference IQA scores and an exposure [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Overview of our quality assessment agent. We compute no-reference IQA signals and an exposure [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

read the original abstract

Vision-Language Models (VLMs) have significantly advanced automated Radiology Report Generation (RRG). However, existing methods implicitly assume high-quality inputs, overlooking the noise and artifacts prevalent in real-world clinical environments. Consequently, current models exhibit severe performance degradation when processing suboptimal images. To bridge this gap, we propose a robust report generation framework explicitly designed for image quality variations. We first introduce an Automated Quality Assessment Agent (AQAA) to identify low-quality samples within the MIMIC-CXR dataset and establish the Low-quality Radiology Report Generation (LRRG) benchmark. To tackle degradation-induced shifts, we propose a novel Dual-loop Training Strategy leveraging bi-level optimization and gradient consistency. This approach ensures the model learns quality-agnostic diagnostic features by aligning gradient directions across varying quality regimes. Extensive experiments demonstrate that our approach effectively mitigates model performance degradation caused by image quality deterioration. The code and data will be released upon acceptance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper adds a low-quality X-ray subset benchmark and a bi-level gradient alignment trick for report generation, but the abstract supplies no numbers and the alignment step risks smoothing away subtle diagnostic cues.

read the letter

The main things here are the LRRG benchmark pulled from MIMIC-CXR via their automated quality agent and the dual-loop bi-level optimization that forces gradient consistency between high- and low-quality regimes. Both are new relative to standard RRG fine-tuning or augmentation work. The practical motivation is sound: real clinical X-rays often carry noise and artifacts that tank current models, and most papers ignore that gap. Releasing the data and code is also a plus for anyone who wants to test robustness directly. The bi-level setup is the clearest technical contribution, aiming to push the model toward features that hold up across quality levels rather than overfitting to clean inputs. That direction makes sense on paper. The soft spot is whether the gradient alignment actually keeps all the clinically useful signal. Forcing consistency can act like a strong regularizer, and it is easy to imagine it attenuating fine details such as small nodules or faint interstitial patterns that only appear clearly on higher-quality scans. The abstract claims mitigation of degradation but gives no quantitative results, no ablations on high-quality performance, and no check on whether the learned features remain complete. Without those numbers it is hard to know if the method solves the problem or just trades one failure mode for another. This is aimed at groups working on deployable medical VLMs who already have RRG pipelines and need to handle variable image quality. A reader who wants a new benchmark to stress-test their own model will get immediate value from the LRRG set. The work deserves a serious referee because the problem is real, the benchmark is fresh, and the training idea is distinct, even though the current write-up leaves the central claim unverified. I would send it out with a request for the missing ablations and high-quality retention metrics.

Referee Report

2 major / 2 minor

Summary. The paper claims to address performance degradation in vision-language models for radiology report generation (RRG) when inputs are low-quality X-ray images. It introduces an Automated Quality Assessment Agent (AQAA) to identify low-quality samples in MIMIC-CXR and create the Low-quality Radiology Report Generation (LRRG) benchmark. The main technical contribution is a Dual-loop Training Strategy based on bi-level optimization that enforces gradient consistency across quality regimes to learn quality-agnostic diagnostic features. The authors state that extensive experiments show the method effectively mitigates degradation due to image quality deterioration.

Significance. If the claimed results hold with proper quantitative support, the work tackles a practically relevant issue for real-world clinical deployment of automated RRG systems, where image quality often varies. The LRRG benchmark could provide a useful testbed for robustness research. The bi-level optimization idea is a reasonable attempt to handle quality-induced shifts without explicit domain adaptation. However, the current presentation supplies no metrics, ablations, or controls, so the significance cannot yet be assessed.

major comments (2)

[Abstract] Abstract: the central claim that 'extensive experiments demonstrate that our approach effectively mitigates model performance degradation' is unsupported because the abstract (and available description) contains no quantitative metrics (e.g., BLEU, ROUGE, F1, or clinical accuracy scores), no baseline comparisons, and no ablation results. This absence is load-bearing for the effectiveness claim and prevents verification of whether the bi-level strategy succeeds or introduces new failure modes.
[Dual-loop Training Strategy] Dual-loop Training Strategy: the bi-level optimization that aligns gradient directions between low- and high-quality regimes may suppress subtle, clinically relevant signals visible only in higher-quality images (e.g., fine interstitial markings or small nodules). The manuscript must demonstrate that high-quality performance is preserved and that the resulting features remain diagnostically complete; without such evidence the quality-agnostic claim is at risk.

minor comments (2)

[Abstract] The acronym AQAA is used in the abstract without prior expansion; expand on first use.
[Benchmark Construction] The description of the LRRG benchmark creation lacks details on the quality assessment criteria or inter-rater agreement if any human validation was performed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for stronger quantitative support and safeguards against feature suppression. We agree that the abstract requires explicit metrics and that additional evidence on high-quality performance is warranted. Both points can be addressed through targeted revisions to the presentation and experiments without altering the core claims or methodology.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that 'extensive experiments demonstrate that our approach effectively mitigates model performance degradation' is unsupported because the abstract (and available description) contains no quantitative metrics (e.g., BLEU, ROUGE, F1, or clinical accuracy scores), no baseline comparisons, and no ablation results. This absence is load-bearing for the effectiveness claim and prevents verification of whether the bi-level strategy succeeds or introduces new failure modes.

Authors: We agree the abstract should be self-contained with key results. The full manuscript reports these metrics (BLEU-4, ROUGE-L, F1, and clinical accuracy) with baseline comparisons and ablations on the LRRG benchmark in Section 4. In revision we will insert concise quantitative statements into the abstract, e.g., 'Our method improves BLEU-4 by X points over baselines on low-quality images while preserving Y on high-quality inputs.' revision: yes
Referee: [Dual-loop Training Strategy] Dual-loop Training Strategy: the bi-level optimization that aligns gradient directions between low- and high-quality regimes may suppress subtle, clinically relevant signals visible only in higher-quality images (e.g., fine interstitial markings or small nodules). The manuscript must demonstrate that high-quality performance is preserved and that the resulting features remain diagnostically complete; without such evidence the quality-agnostic claim is at risk.

Authors: This is a valid concern. The gradient-consistency term is formulated to align only on shared diagnostic directions and does not penalize quality-specific signals. We will add a dedicated table in the revision showing performance on the original high-quality MIMIC-CXR test split, confirming no degradation relative to the baseline. We will also include qualitative report examples and Grad-CAM visualizations on subtle findings to demonstrate preservation of diagnostically complete features. revision: yes

Circularity Check

0 steps flagged

No circularity: novel bi-level training strategy presented as independent algorithmic contribution

full rationale

The paper introduces an Automated Quality Assessment Agent and a Dual-loop Training Strategy based on bi-level optimization with gradient consistency to produce quality-agnostic features. No equations, derivations, or fitted parameters are shown that reduce by construction to the inputs. The training approach is described as a new method rather than a re-expression of existing quantities or self-citations. The central claim of mitigating degradation is framed as an empirical result from experiments, with no load-bearing self-citation chains or self-definitional steps. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the approach rests on standard bi-level optimization and gradient alignment concepts from the broader machine-learning literature.

pith-pipeline@v0.9.0 · 5475 in / 1006 out tokens · 37067 ms · 2026-05-10T17:03:24.022402+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

42 extracted references · 3 canonical work pages · 2 internal anchors

[1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. Gpt-4 technical report. arXiv:2303.08774

work page internal anchor Pith review Pith/arXiv arXiv 2023
[4]

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, pages 65--72

2004
[5]

Shenshen Bu, Taiji Li, Yuedong Yang, and Zhiming Dai. 2024. Instance-level expert knowledge and aggregate discriminative attention for radiology report generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14194--14204

2024
[6]

Chaofeng Chen, Jiadi Mo, Jingwen Hou, Haoning Wu, Liang Liao, Wenxiu Sun, Qiong Yan, and Weisi Lin. 2024. Topiq: A top-down approach from semantics to distortions for image quality assessment. IEEE Transactions on Image Processing, 33:2404--2418

2024
[7]

Xiangjun Chen, Zhiyuan Lou, Xiaoxiang Gao, Lu Yin, Siyu Qin, Muyang Lin, Fangao Zhang, Yi Lu, Shichao Ding, Ruixiao Liu, and 1 others. 2025. A noise-tolerant human--machine interface based on deep learning-enhanced wearable sensors. Nature Sensors, pages 1--13

2025
[8]

Zhihong Chen, Yan Song, Tsung-Hui Chang, and Xiang Wan. 2020. Generating radiology reports via memory-driven transformer. In EMNLP, pages 1439--1449

2020
[9]

Zijie Cheng, Ariel Yuhan Ong, Siegfried K Wagner, David A Merle, Lie Ju, Hanyuan Zhang, Ruinian Chen, Linze Pang, Boxuan Li, Tiantian He, and 1 others. 2025. Understanding the robustness of vision-language models to medical image artefacts. npj Digital Medicine, 8(1):727

2025
[10]

Yashin Dicente Cid, Matthew Macpherson, Louise Gervais-Andre, Yuanyi Zhu, Giuseppe Franco, Ruggiero Santeramo, Chee Lim, Ian Selby, Keerthini Muthuswamy, Ashik Amlani, and 1 others. 2024. Development and validation of open-source deep neural networks for comprehensive chest X -ray reading: a retrospective, multicentre study. The Lancet Digital Health, 6(1...

2024
[11]

Fei Dong, Shouping Nie, Manling Chen, Fangfang Xu, and Qian Li. 2025. Keyword-based ai assistance in the generation of radiology reports: A pilot study. npj Digital Medicine, 8(1):490

2025
[12]

Terri L Fauber. 2020. Radiographic Imaging and Exposure-E-Book: Radiographic Imaging and Exposure-E-Book. Elsevier Health Sciences

2020
[13]

Tiancheng Gu, Kaicheng Yang, Xiang An, Ziyong Feng, Dongnan Lin, and Weidong Cai. 2025. ORID : Organ-regional information driven framework for radiology report generation. In WACV, pages 378--387

2025
[14]

Wenjun Hou, Yi Cheng, Kaishuai Xu, Heng Li, Yan Hu, Wenjie Li, and Jiang Liu. 2025. Radar: Enhancing radiology report generation with supplementary knowledge injection. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), page 26366–26381

2025
[15]

Luzhe Huang, Yuzhu Li, Nir Pillar, Tal Keidar Haran, William Dean Wallace, and Aydogan Ozcan. 2025. A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology. Nature Biomedical Engineering, pages 1--19

2025
[16]

Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Du Nguyen Duong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P Lungren, Andrew Y Ng, and 1 others. 2021. Radgraph: Extracting clinical entities and relations from radiology reports. arXiv preprint arXiv:2106.14463

work page arXiv 2021
[17]

Jaehwan Jeong, Katherine Tian, Andrew Li, Sina Hartung, Subathra Adithan, Fardad Behzadi, Juan Calle, David Osayande, Michael Pohlen, and Pranav Rajpurkar. 2024. Multimodal image-text matching improves retrieval-based chest X -ray report generation. In MIDL, pages 978--990

2024
[18]

Haibo Jin, Haoxuan Che, Sunan He, and Hao Chen. 2025. A chain of diagnosis framework for accurate and explainable radiology report generation. IEEE Transactions on Medical Imaging

2025
[19]

Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng. 2019. MIMIC-CXR , a de-identified publicly available database of chest radiographs with free-text reports. Scientific Data, 6(1):317

2019
[20]

Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. 2021. Musiq: Multi-scale image quality transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5148--5157

2021
[21]

Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao. 2023. LLaVA-Med : Training a large language-and-vision assistant for biomedicine in one day. NeurIPS, 36:28541--28564

2023
[22]

Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. 2018. Learning to generalize: Meta-learning for domain generalization. In AAAI Conference on Artificial Intelligence

2018
[23]

C Lin. 2005. Recall-oriented understudy for gisting evaluation (rouge). Retrieved August, 20:2005

2005
[24]

Fenglin Liu, Zheng Li, Qingyu Yin, Jinfa Huang, Jiebo Luo, Anshul Thakur, Kim Branson, Patrick Schwab, Bing Yin, Xian Wu, and 1 others. 2025. A multimodal multidomain multilingual medical foundation model for zero shot clinical diagnosis. npj Digital Medicine, 8(1):86

2025
[25]

Fenglin Liu, Xian Wu, Shen Ge, Wei Fan, and Yuexian Zou. 2021. Exploring and distilling posterior and prior knowledge for radiology report generation. In CVPR, pages 13753--13762

2021
[26]

Yizhen Luo, Jiahuan Zhang, Siqi Fan, Kai Yang, Massimo Hong, Yushuai Wu, Mu Qiao, and Zaiqing Nie. 2024. BiomedGPT : An open multimodal large language model for biomedicine. IEEE Journal of Biomedical and Health Informatics

2024
[27]

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

2002
[28]

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, and 1 others. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748--8763. PmLR

2021
[29]

Pranav Rajpurkar, Jeremy Irvin, Robyn L Ball, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis P Langlotz, and 1 others. 2018. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine, 15(11):e1002686

2018
[30]

Jarrel CY Seah, Cyril HM Tang, Quinlan D Buchlak, Xavier G Holt, Jeffrey B Wardman, Anuar Aimoldin, Nazanin Esmaili, Hassan Ahmad, Hung Pham, John F Lambert, and 1 others. 2021. Effect of a comprehensive deep-learning model on the accuracy of chest X -ray interpretation by radiologists: a retrospective, multireader multicase study. The Lancet Digital Heal...

2021
[31]

Tim Tanida, Philip M \"u ller, Georgios Kaissis, and Daniel Rueckert. 2023. Interactive and explainable region-guided radiology report generation. In CVPR, pages 7433--7442

2023
[32]

Ryutaro Tanno, David GT Barrett, Andrew Sellergren, Sumedh Ghaisas, Sumanth Dathathri, Abigail See, Johannes Welbl, Charles Lau, Tao Tu, Shekoofeh Azizi, and 1 others. 2025. Collaboration between clinicians and vision--language models in radiology report generation. Nature Medicine, 31(2):599--608

2025
[33]

Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. 2023 a . Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 2555--2563

2023
[34]

Xiao Wang, Fuling Wang, Yuehang Li, Qingchuan Ma, Shiao Wang, Bo Jiang, and Jin Tang. 2025 a . CXPMRG-Bench : Pre-training and benchmarking for X -ray medical report generation on CheXpert Plus dataset. In CVPR, pages 5123--5133

2025
[35]

Xinyi Wang, Grazziela Figueredo, Ruizhe Li, Wei Emma Zhang, Weitong Chen, and Xin Chen. 2025 b . A survey of deep-learning-based radiology report generation using multimodal inputs. Medical Image Analysis, page 103627

2025
[36]

Zhanyu Wang, Lingqiao Liu, Lei Wang, and Luping Zhou. 2023 b . METransformer : Radiology report generation by transformer with multiple learnable expert tokens. In CVPR, pages 11558--11567

2023
[37]

Zhanyu Wang, Lingqiao Liu, Lei Wang, and Luping Zhou. 2023 c . R2GenGPT : Radiology report generation with frozen LLMs . Meta-Radiology, 1(3):100033

2023
[38]

Justin Xu, Xi Zhang, Javid Abderezaei, Julie Bauml, Roger Boodoo, Fatemeh Haghighi, Ali Ganjizadeh, Eric Brattain, Dave Van Veen, Zaiqiao Meng, and 1 others. 2025. Radeval: A framework for radiology text evaluation. In EMNLP, pages 546--557

2025
[39]

An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388

work page internal anchor Pith review Pith/arXiv arXiv 2025
[40]

Lin Zhang, Lei Zhang, and Alan C Bovik. 2015. A feature-enriched completely blind image quality evaluator. IEEE Transactions on Image Processing, 24(8):2579--2591

2015
[41]

Xi Zhang, Zaiqiao Meng, Jake Lever, and Edmond S. L. Ho. 2025. Libra: Leveraging temporal images for biomedical radiology analysis. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17275--17303

2025
[42]

Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. 2024. Ratescore: A metric for radiology report generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15004--15019

2024