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REVIEW 3 major objections 6 minor 30 references

A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor

T0 review · 3 major / 6 minor · reviewed 2026-07-30 · grok-4.5

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Pith's one-line read A single lightweight web dashboard runs brain-tumor radiomics from cohort tables through feature extraction to guarded ML prediction while keeping intermediate steps visible.

desk verdict Credible V0 engineering report of a Dash radiomics dashboard with sensible guardrails; benefit language outruns the evidence, but the architecture itself is coherent and worth a referee look if claims are narrowed. read the letter →

arxiv 2607.26834 v1 pith:5ENG3MUX submitted 2026-07-29 cs.SE

Yin Lin , Elena De Martin , Giacomo Conte , Domenico Aquino , Cristiana Pedone , Alberto Redaelli , Riccardo Barbieri , Laura Fariselli
show 1 more author
Simona Ferrante
This is my paper · ORCID
classification cs.SE
keywords braintumorsradiomicsmachinelearningclinicaldecisionsupportvisualanalyticsneuro-oncologyexplainableAI
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

AI and radiomics for brain tumors often fail to reach the clinic because pipelines are scattered, hard to inspect, and poorly matched to how clinicians work. This paper presents version zero of a portable web visual-analytics system that puts three jobs in one interface: manage cohorts from clinical tables, extract radiomic features from MRI volumes and segmentation masks, and run pre-trained models only under explicit safety checks. Intermediate artifacts stay on screen so users can see what was filtered, what features were produced, and how they were joined to clinical data. The system was shaped through iterative clinician feedback and exercised on a public glioblastoma cohort and a proprietary meningioma cohort. The authors argue that this inspectable, low-infrastructure design is a practical bridge from lab models to responsible clinical use.

What carries the argument

The V0 three-module dashboard (Data, Radiomics, Prediction): it appends PyRadiomics features into the active clinical table by patient ID and blocks inference with a visible warning when the selected model is incompatible with the chosen dataset.

What would settle it

A controlled clinician study comparing this dashboard to current tools on the same radiomics-to-prediction tasks, scoring mismatched-model error rates, time to verify intermediate features, and trust ratings—if those measures do not improve, the central claim fails.

Watch

Extended reading notes

Core claim

The authors establish that a scalable web-based visual analytics system can integrate cohort management, radiomic feature extraction and fusion, and guarded inference with pre-trained models in one interface, and that explicitly exposing the intermediate workflow artifacts improves traceability, interpretability, and responsible use of AI in brain-tumor analysis.

Load-bearing premise

Describing the modules on two datasets and iterating with clinician feedback is enough evidence that exposing intermediate steps truly improves traceability, interpretability, and responsible use.

Editorial extensions

If this is right

  • Clinics with limited CPU and RAM can run radiomics inference without a heavy dedicated backend.
  • Explicit model–dataset guardrails reduce silent wrong predictions in neuro-oncology.
  • Inspectable cohort filters and feature tables create an auditable path from data to prediction.
  • The same lightweight web pattern can be extended once DICOM support and faster extraction are added.
  • Pre-trained models become usable at the point of care without forcing clinicians to reassemble fragmented scripts.

Reading between the lines

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

  • The guardrail pattern—block and explain mismatch rather than emit a score—could transfer to other imaging ML settings where wrong model application is a safety risk.
  • Without reported task-time or error-rate metrics, claims of improved responsible use will stay hard to compare against existing imaging workbenches.
  • Caching and asynchronous extraction, already flagged as future work, are likely required before routine use on ordinary clinical workstations.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The manuscript presents V0 of a lightweight, Dash-based web visual analytics system for radiomics-driven ML inference in neuro-oncology. It integrates three modules in one interface—Data (CSV/XLSX cohort ingest, filtering, export, subject registration), Radiomics (NIfTI image/mask upload, PyRadiomics extraction, append-to-database by patient ID), and Prediction (on-demand inference with pre-trained models plus mismatch guardrails that block incompatible model–dataset pairs). Design followed an iterative user-centred process (pipeline decomposition, format analysis, component design, deployment under clinical hardware constraints, clinician feedback). The system is demonstrated on BraTS 2020 (n=236 glioblastoma) and a proprietary INCB skull-base meningioma cohort (n=104). The central claim is that explicit exposure of intermediate workflow artifacts plus guardrails improves traceability, interpretability, and responsible AI use, offering a portable foundation for clinically oriented brain-tumor AI.

Significance. If the contribution is read as an engineering systems report—a portable, inspectable end-to-end radiomics–ML dashboard matched to real clinical workstation constraints—it is a useful and under-served piece of translational infrastructure. Related work correctly notes that most tools stop at dashboards, labelling, or segmentation (e.g., 3D Slicer, MONAI Label) rather than chaining cohort management, feature extraction/fusion, and guarded inference in one interactive system. Strengths include a coherent three-module architecture, explicit intermediate artifacts, inference guardrails, and a deployment model (Dash, no heavy backend) aligned with limited clinical hardware. The paper does not ship quantitative usability evidence, open code/artifacts in the text, or controlled comparisons; significance therefore rests on the systems design and the gap it targets, not on measured clinical impact.

major comments (3)
  1. [Abstract; §IV; §III.B.5; §V] Abstract and §I claim the system was “evaluated on” BraTS 2020 and the INCB cohort and that explicit exposure of intermediate artifacts “improves traceability, interpretability, and responsible use of AI.” §IV (Results) only describes UI behaviour (filters, preview tables, Append to Database, orange mismatch banner); no task-time, error-rate, trust, auditability, or usability metrics, and no before/after or comparison to 3D Slicer / existing radiomics viewers, are reported. §III.B step 5 mentions clinician feedback sessions without protocol, themes, or outcomes. Either add a minimal evaluation (even qualitative thematic summary or small task-based study) or revise the abstract/contribution language to “demonstrated/deployed on” and frame benefit claims as design goals rather than established results.
  2. [§I contributions; §III.B.3; §IV Prediction module] The Prediction module is described as enabling “on-demand inference using the embedded models developed in this project,” yet the manuscript never specifies which models, targets (survival? volumetric response?), training protocol, performance, or how SHAP/explainability (promised in §I contributions and pipeline analysis) is surfaced in V0. §III.B.3 states models are assumed pre-trained and reusable, and preprocessing/training are out of scope—fine for a systems paper—but without naming the embedded artifacts, inputs/outputs, and what the user actually sees at inference time, the “guarded inference” and “explainable pipelines” claims cannot be assessed or reproduced. Add a short subsection or table listing model cards (task, features required, performance on the two cohorts, explanation modality if any).
  3. [§II Related Works] §II asserts that a coherent system implementing the full inspectable radiomics–ML chain “remains unaddressed.” That gap claim is load-bearing for novelty. Several cited and uncited tools (3D Slicer radiomics extensions, Severn et al.’s explainable radiomics pipeline [23], various research dashboards) partially cover extraction + explanation. The manuscript should more carefully delineate what is new in V0 (single web app, cohort↔radiomics join, inference guardrails, clinical-hardware deployment model) versus what is incremental, so the contribution boundary is falsifiable rather than absolute.
minor comments (6)
  1. [Title; Abstract; §I; §IV] Title and abstract say “Explainable” / “explainability (e.g., via SHAP),” but §IV does not describe any explanation UI in V0. Align title/abstract with implemented scope or move SHAP to future work explicitly in Results.
  2. [Table I; §III.A] Table I header says “BRATS2020” and reports n=236; BraTS 2020 training set is conventionally larger—clarify the exact subset and inclusion criteria (e.g., subjects with survival labels only).
  3. [Figure 1] Figure 1 is described but not available in the text package; ensure the bottom-right screenshot legibly shows the three modules and guardrail banner for camera-ready.
  4. [Throughout] Minor typos/spacing: “cli nical,” “tran s-parency,” “inf erence,” “structur ed,” “pre-tra ined,” “coh ort,” “workflow,” “F ondazione,” author list formatting. Copy-edit for line-break artifacts from the PDF.
  5. [Index Terms] Index terms and keywords are appropriate; consider adding “visual analytics” and “clinical decision support systems” for discoverability in cs.SE / medical informatics venues.
  6. [§V] §V limitations correctly flag DICOM, extraction latency, and incomplete coverage—good. A brief sentence on data governance / PHI handling for a hospital-deployed web app would strengthen the translational discussion.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: systems/UI description with no prediction-equals-fit or self-definitional loop.

full rationale

This paper is an engineering report of a Dash-based V0 dashboard (Data, Radiomics, Prediction modules) for radiomics ML in brain tumors. There is no mathematical derivation chain, no fitted parameter renamed as an independent prediction, and no uniqueness theorem or ansatz imported from the authors’ prior work to force the central claim. Evaluation is descriptive deployment on BraTS2020 and an INCB cohort plus iterative clinician feedback; the benefit language (traceability, interpretability, responsible use) is under-supported by metrics, but that is an evidence gap, not circularity by construction. Self-citations [6][7] concern the authors’ prior survival models that may be embedded for inference; they do not underwrite the system architecture or make the UI’s existence equivalent to its claimed clinical benefit. The derivation is self-contained as a software description against external datasets and stated design constraints. Score 0; steps empty.

Assumptions & free parameters 0 free parameters · 5 assumptions · 1 invented entities

Load-bearing premises are engineering and clinical-process assumptions, not fitted physical constants. The central claim rests on standard web-stack feasibility, adequacy of excluding train-time ML from scope, format choices, and the unproven leap from inspectable artifacts to responsible clinical AI use.

assumptions (5)
  • domain assumption Pre-trained ML models are available as reusable artifacts and inference-only use is the right clinical-facing scope (training/validation nested CV stay outside the app).
    Stated in §III.B.3 logical design; shapes what ‘pipeline support’ means in V0.
  • domain assumption Clinical workstations have limited CPU/RAM so a lightweight Dash web app without dedicated backend services is the appropriate deployment model.
    §III.B.4 implementation constraints; justifies architecture and portability claims.
  • domain assumption NIfTI images/masks plus CSV/XLSX clinical tables are sufficient input abstractions for the targeted workflows.
    §III.B.2; Discussion later admits DICOM gap, so the axiom limits generalizability.
  • ad hoc to paper Explicit UI exposure of intermediate artifacts (tables, filters, feature previews) plus mismatch guardrails improves traceability, interpretability, and responsible use.
    Core contribution in Abstract/Discussion; not measured with user-study statistics in this manuscript.
  • domain assumption Iterative clinician feedback sessions are adequate validation for releasing V0 as a foundation for clinically oriented AI.
    §III.B.5 design process; qualitative loop without reported instruments or sample size of feedback.
invented entities (1)
  • V0 radiomics visual analytics system (Data / Radiomics / Prediction modules with inference guardrails)
    purpose: Unify cohort management, feature extraction/merge, and safeguarded prediction in one inspectable web interface.
    The paper’s primary postulated artifact; existence is author-reported implementation, not independently shipped for external inspection.

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Cite this review

Pith. "Pith review of A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor." pith.science (2026). https://pith.science/paper/5ENG3MUX

@misc{pith2026260726834,
  author       = {Pith},
  title        = {Pith review of: A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5ENG3MUX}},
  note         = {Machine review of arXiv:2607.26834}
}
read the original abstract

Artificial intelligence and radiomics are increasingly used in brain tumor research, yet their translation into clinical practice remains limited by fragmented workflows, poor transparency, and weak integration with end users' needs. We present the first version of a scalable web-based visual analytics system designed to support radiomics-driven machine learning inference in neuro-oncology. The platform integrates three core functions within a single interface: cohort management from structured clinical tables, radiomic feature extraction from medical images and segmentation masks, and guarded inference with pre-trained machine learning models. The system was developed through an iterative user-centred design process and evaluated on both a public glioblastoma dataset and a proprietary clinical cohort. A key contribution is the explicit exposure of intermediate workflow artifacts, which improves traceability, interpretability, and responsible use of AI. By combining portability, inspectability, and deployment simplicity, the proposed framework offers a practical foundation for clinically oriented AI applications in brain tumor analysis.

Figures

Figures reproduced from arXiv: 2607.26834 by the authors.

Figure 1
Figure 1. Overview of the proposed system. Upper panel: the com [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

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Reference graph

Works this paper leans on

30 extracted references · 1 canonical work pages

  1. [23]

    A pipeline for the implementation and visualization of expla inable machine learning for medical imaging using radiomics features,

    C. Severn, K. Suresh, C. G¨ org, Y . Choi, R. Jain, and D. Gh osh, “A pipeline for the implementation and visualization of expla inable machine learning for medical imaging using radiomics features,” Sensors, vol. 22, no. 14, Art. no. 5205, 2022

    2022

  2. [1]

    An integrative model of cellular states, plasticity, and g enetics for glioblastoma,

    C. Neftel, J. Laffy, M. G. Filbin, T. Hara, M. E. Shore, G. J . Rahme, A. R. Richman, D. Silverbush, M. L. Shaw, C. M. Hebert, et al., “An integrative model of cellular states, plasticity, and g enetics for glioblastoma,” Cell, vol. 178, no. 4, pp. 835–849, 2019

    2019

  3. [2]

    A nationwide population-based study on overall survival aft er meningioma surgery,

    C. Champeaux-Depond, J. Weller, S. Froelich, and M. Resc he-Rigon, “A nationwide population-based study on overall survival aft er meningioma surgery,” Cancer Epidemiology, vol. 70, Art. no. 101875, 2021

    2021

  4. [3]

    Radiotherapy plus concomitant and adjuvant temozo lomide for glioblastoma,

    R. Stupp, W. P . Mason, M. J. van den Bent, M. Weller, B. Fish er, M. J. B. Taphoorn, K. Belanger, A. A. Brandes, C. Marosi, U. Bogd ahn, et al., “Radiotherapy plus concomitant and adjuvant temozo lomide for glioblastoma,” New England Journal of Medicine , vol. 352, no. 10, pp. 987–996, 2005

    2005

  5. [4]

    Artifici al intelligence-based biomarkers for treatment decisions in oncology,

    M. Ligero, O. El Nahhas, M. Aldea, and J. Kather, “Artifici al intelligence-based biomarkers for treatment decisions in oncology,” Trends in Cancer , vol. 11, pp. 232–244, 2025

    2025

  6. [5]

    Radiomics in medical imaging—“how-to

    J. E. van Timmeren, D. Cester, S. Tanadini-Lang, H. Alkad hi, and B. Baessler, “Radiomics in medical imaging—“how-to” guide an d critical reflection,” Insights into Imaging , vol. 11, Art. no. 91, 2020

    2020

  7. [6]

    Glioblastoma overall survival prediction with vision transformers,

    Y . Lin, R. Barbieri, D. Aquino, G. Lauria, M. Grisoli, E. D e Momi, A. Redaelli, and S. Ferrante, “Glioblastoma overall survival prediction with vision transformers,” in Proc. 47th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC) , 2025, pp. 1–4

    2025

  8. [7]

    Lightweight ensemble vision transformer fra mework for non-invasive survival prediction in glioblastoma,

    Y . Lin, D. Aquino, G. Lauria, M. Grisoli, A. Redaelli, R. B arbieri, and S. Ferrante, “Lightweight ensemble vision transformer fra mework for non-invasive survival prediction in glioblastoma,” Neurocomputing, Art. no. 133303, Jun. 2026, doi: 10.1016/j.neucom.2026.133303

    arXiv 2026

Show all 30 references
  1. [8]

    Radiomics: Extracting more information fr om medical images using advanced feature analysis,

    P . Lambin, E. Rios-V elazquez, R. Leijenaar, S. Carvalho , R. G. P . M. van Stiphout, P . Granton, C. M. L. Zegers, R. Gillies, R. Boel lard, A. Dekker, et al., “Radiomics: Extracting more information fr om medical images using advanced feature analysis,” European Journal of ...

    2012

  2. [9]

    Radiomic profiling of glioblastoma: Identifying an imagin g predictor of patient survival with improved performance over establi shed clinical and radiologic risk models,

    P . Kickingereder, S. Burth, A. Wick, M. G¨ otz, O. Eidel, H .-P . Schlem- mer, K. Maier-Hein, W. Wick, M. Bendszus, A. Radbruch, et al. , “Radiomic profiling of glioblastoma: Identifying an imagin g predictor of patient survival with improved performance over establi shed clin...

    2016

  3. [10]

    Investigating a quantitative radiomics approach for brain tumor classifica tion,

    A. Abidin, I. Dar, A. D’Souza, E. Lin, and A. Wism ¨ uller, “Investigating a quantitative radiomics approach for brain tumor classifica tion,” in Med- ical Imaging 2019: Biomedical Applications in Molecular , S tructural, and Functional Imaging , vol. 10953, 2019, pp. 36–45

    2019

  4. [11]

    Le- sion location implemented magnetic resonance imaging radi omics for predicting IDH and TERT promoter mutations in grade II/III g liomas,

    H. Arita, M. Kinoshita, A. Kawaguchi, M. Takahashi, Y . N arita, Y . Terakawa, N. Tsuyuguchi, Y . Okita, M. Nonaka, S. Moriuchi, et al., “Le- sion location implemented magnetic resonance imaging radi omics for predicting IDH and TERT promoter mutations in grade II/III g liomas...

    2018 doi

  5. [12]

    AI and machine learning in medical imaging: Key points from development to translation,

    R. Samala, K. Drukker, A. Shukla-Dave, H.-P . Chan, B. Sa hiner, N. Petrick, H. Greenspan, U. Mahmood, R. M. Summers, G. Tourass i, et al., “AI and machine learning in medical imaging: Key points from development to translation,” BJR|Artificial Intelligence, vol. 1, Art. no. ub...

    2024

  6. [13]

    The widen ing gap between radiomics research and clinical translation: Reth inking current practices and shared responsibilities,

    B. Kocak, D. Pinto dos Santos, and M. Dietzel, “The widen ing gap between radiomics research and clinical translation: Reth inking current practices and shared responsibilities,” European Journal of Radiology Artificial Intelligence, vol. 1, Art. no. 100004, Jan. 2025

    2025

  7. [14]

    Data v isualization support for interdisciplinary team treatment planning in c linical oncol- ogy: Scoping review,

    D. Boehm, C. Strantz, A. Ustjanzew, I. Manuilova, A. Sch eiter, T. Pauli, N. Hechtel, N. Reimer, J. Christoph, H. Busch, et al., “Data v isualization support for interdisciplinary team treatment planning in c linical oncol- ogy: Scoping review,” Journal of Medical Internet Res...

    2025 doi

  8. [15]

    State-of-the -art dashboards on clinical indicator data to support reflection on practice : Scoping review,

    B. Bucalon, T. Shaw, K. Brown, and J. Kay, “State-of-the -art dashboards on clinical indicator data to support reflection on practice : Scoping review,” JMIR Medical Informatics , vol. 10, no. 2, Art. no. e32695, 2022

    2022

  9. [16]

    Deve lopment, implementation, and evaluation methods for dashboards in h ealth care: Scoping review,

    D. Helminski, J. Sussman, P . Pfeiffer, A. Kokaly, A. Ran usch, A. Renji, L. Damschroder, Z. Landis-Lewis, and J. K. Kurlander, “Deve lopment, implementation, and evaluation methods for dashboards in h ealth care: Scoping review,” JMIR Medical Informatics , vol. 12, Art. no. e...

    2024

  10. [17]

    De velopment of an electronic health record-integrated patient-report ed outcome-based shared decision-making dashboard in oncology,

    N. Mohindra, S. Garcia, S. Kircher, C. Barnard, L. Perry , M. Lyleroehr, A. Coughlin, V . Morken, R. Chmiel, L. Hirschhorn, et al., “De velopment of an electronic health record-integrated patient-report ed outcome-based shared decision-making dashboard in oncology,” JAMIA Open...

    2024

  11. [18]

    Perry, V

    L. Perry, V . Morken, J. Peipert, B. Y anez, S. Garcia, C. B arnard, L. Hirschhorn, J. Linder, N. Jordan, R. Ackermann, et al., “Pat ient-reported outcome dashboards within the electronic health record to s upport shared decision-making: Protocol for co-design and clinic al ev...

    2022

  12. [19]

    Empowering personalized oncology: Evolution of digital support and vi sualization tools for molecular tumor boards,

    C. Strantz, D. B¨ ohm, T. Ganslandt, M. B¨ orries, P . Metzger, T. Pauli, A. Blaumeiser, A. Scheiter, I.-C. Jung, J. Christoph, et al., “ Empowering personalized oncology: Evolution of digital support and vi sualization tools for molecular tumor boards,” BMC Medical Informatic...

    2025

  13. [20]

    MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images,

    A. Diaz-Pinto, S. Alle, V . Nath, Y . Tang, A. Ihsani, M. As ad, F. P´ erez- Garc´ ıa, P . Mehta, W. Li, M. Flores, et al., “MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images, ” Medical Image Analysis , vol. 95, Art. no. 103207, 2024

    2024

  14. [21]

    3D Slicer as an image computing platform for the Quantitative Imaging Network,

    A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J .-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, et al. , “3D Slicer as an image computing platform for the Quantitative Imaging Network,” Magnetic Resonance Imaging , vol. 30, no. 9, pp. 1323–1...

    2012

  15. [22]

    Brain tumor detection and segmentation: Interactive fram ework with a visual interface and feedback facility for dynamically imp roved accuracy and trust,

    K. Sailunaz, D. Bestepe, S. Alhajj, T. ¨Ozyer, J. Rokne, and R. Alhajj, “Brain tumor detection and segmentation: Interactive fram ework with a visual interface and feedback facility for dynamically imp roved accuracy and trust,” PLOS ONE , vol. 18, no. 4, Art. no. e0284418, 2023

    2023

  16. [24]

    3D Slicer as a tool for interac tive brain tumor segmentation,

    R. Kikinis and S. Pieper, “3D Slicer as a tool for interac tive brain tumor segmentation,” in Proc. 2011 Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC) , 2011, pp. 6982–6984

    2011

  17. [25]

    AI-assisted seg mentation tool for brain tumor MR image analysis,

    M. Lee, J. H. Kim, W. Choi, and K. H. Lee, “AI-assisted seg mentation tool for brain tumor MR image analysis,” Journal of Imaging Informatics in Medicine , vol. 38, pp. 74–83, 2025

    2025

  18. [26]

    T he multimodal brain tumor image segmentation benchmark (BRA T S),

    B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. F arahani, J. Kirby, Y . Burren, N. Porz, J. Slotboom, R. Wiest, et al., “T he multimodal brain tumor image segmentation benchmark (BRA T S),” IEEE Transactions on Medical Imaging , vol. 34, no. 10, pp. 1993–2024, 2015

    1993

  19. [27]

    Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentati on labels and radiomic features,

    S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos, “Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentati on labels and radiomic features,” Scientific Data , vol. 4, Art. no. 170117, 2017

    2017

  20. [28]

    Identify ing the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRA TS ch allenge,

    S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Cri mi, R. T. Shinohara, C. Berger, S. M. Ha, M. Rozycki, et al., “Identify ing the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRA TS ch al...

    2018 arXiv

  21. [29]

    Developing an au dit and feedback dashboard for family physicians: User-center ed design process,

    J. Shuldiner, T. Kiran, P . Agarwal, M. Daneshvarfard, K . Eldridge, S. Kim, M. Greiver, I. Jokhio, and N. M. Ivers, “Developing an au dit and feedback dashboard for family physicians: User-center ed design process,” JMIR Human Factors , vol. 10, Art. no. e47718, 2023

    2023

  22. [30]

    Improving br ain tumor classification efficacy through the application of feature s election and ensemble classifiers,

    Y . Y uhandri, A. Windarto, and M. Siregar, “Improving br ain tumor classification efficacy through the application of feature s election and ensemble classifiers,” Journal of Image and Graphics , vol. 11, pp. 397– 404, 2023

    2023

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