arxiv: 2605.00892 · v1 · submitted 2026-04-27 · 💻 cs.CV

Recognition: unknown

When To Adapt? Adapting the Model or Data in Federated Medical Imaging

Chamani Shiranthika , Parvaneh Saeedi

Authors on Pith no claims yet

Pith reviewed 2026-05-09 20:29 UTC · model grok-4.3

classification 💻 cs.CV

keywords federated learningmedical imagingdomain shiftpersonalizationharmonizationheterogeneityadaptation strategies

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The pith

Choosing between model personalization and data harmonization in federated medical imaging depends on the type of domain heterogeneity.

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

This paper systematically compares model personalization, which adapts parameters per client, against data harmonization, which reduces input variation across clients, in federated learning setups for medical images. By testing both across six settings involving segmentation and classification tasks with different domain shifts, it identifies that harmonization works better for appearance-based variations while personalization suits structural differences. When client variations are minimal, the approaches perform about the same. Understanding this helps practitioners select the right adaptation method to improve collaborative model training without sharing sensitive data. The work also suggests exploring combined strategies for mixed heterogeneity cases.

Core claim

The paper establishes that the relative effectiveness of data harmonization versus model personalization in addressing domain heterogeneity in federated medical imaging is conditional on the nature of the shifts. Harmonization outperforms when variations are mainly appearance-based, such as in chest X-ray classification for tuberculosis, whereas personalization is superior for structural differences, as in colon polyp segmentation. In cases of limited inter-client variation, both yield similar results. This conclusion comes from a unified evaluation of state-of-the-art methods across six diverse medical imaging tasks covering segmentation and classification.

What carries the argument

A unified evaluation framework that applies multiple harmonization and personalization methods to six medical imaging datasets representing appearance-based and structural domain shifts.

If this is right

Harmonization methods should be selected for tasks dominated by appearance variations like certain classifications.
Personalization approaches are better suited to structural variations in segmentation tasks.
Both strategies are viable when domain differences between clients are small.
Guidelines for method selection can be based on diagnosing whether shifts are appearance or structural in nature.
Hybrid methods combining elements of both may address cases with mixed heterogeneity types.

Where Pith is reading between the lines

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

Clinics could analyze their data distributions beforehand to decide on the adaptation strategy before federated training.
The findings may generalize to non-medical federated learning if similar types of heterogeneity are present.
Developing adaptive systems that switch or combine harmonization and personalization based on detected shift types could be a next step.
Further validation on larger-scale real federated networks with actual institutional data would strengthen the practical guidelines.

Load-bearing premise

The selected six medical imaging settings adequately represent the variety of domain shifts that arise in real federated medical imaging applications.

What would settle it

A counterexample in which a new dataset with primarily appearance-based shifts shows personalization clearly outperforming harmonization would falsify the conditional trade-off claim.

Figures

Figures reproduced from arXiv: 2605.00892 by Chamani Shiranthika, Parvaneh Saeedi.

read the original abstract

Federated learning enables collaborative model training across medical institutions without sharing raw data, but its performance is often limited by domain heterogeneity across clients. Existing approaches to address this challenge fall into two main paradigms: model-side personalization, which adapts model parameters to each client, and data-side harmonization, which reduces inter-client variation at the input level. Despite their widespread use, these strategies have not been systematically compared. In this work, we conduct a comprehensive study across six medical imaging settings-colon polyp, skin lesion, and breast tumor segmentation, and tuberculosis CXR, brain tumor, and breast tumor classification-covering diverse types of domain shift. We evaluate a broad set of state-of-the-art harmonization and personalization methods under a unified framework. Our results reveal a conditional trade-off driven by the nature of heterogeneity: harmonization is more effective when variation is primarily appearance-based (e.g., CXR classification), while personalization performs better when differences are structural (e.g., colon polyp segmentation). When inter-client variation is limited, both strategies perform similarly. These findings demonstrate that the effectiveness of adaptation in federated medical imaging depends on the type and magnitude of domain shift rather than the strategy alone. We provide practical guidelines for selecting between harmonization and personalization and highlight directions for future hybrid approaches that combine both paradigms. Code is available at https://github.com/ChamaniS/WhenToAdapt.

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 runs a useful head-to-head comparison of harmonization versus personalization across six federated medical imaging tasks and offers conditional selection guidelines, though the appearance-versus-structural distinction rests on qualitative labels.

read the letter

The main point is that harmonization works better for appearance-driven shifts like CXR classification while personalization is preferable for structural shifts like polyp segmentation, with both strategies performing similarly when client variation is small. This comes from evaluating several existing methods on colon polyp, skin lesion, and breast tumor segmentation plus tuberculosis CXR, brain tumor, and breast tumor classification tasks under one framework. The breadth of the evaluation and the release of code are the clearest strengths; earlier papers usually tested one paradigm at a time, so the direct comparison produces a practical takeaway that was not already in the literature they cite. The results line up with the stated claim inside these experiments and avoid any obvious circularity or invented quantities. The soft spot is the lack of a measurable proxy for the shift types. The paper assigns the labels by task after the fact without computing something like intensity histogram divergence for appearance or shape variance for structure, so the guideline describes what happened here but does not yet supply a falsifiable criterion that could be applied to new data. Standard experimental details such as statistical tests and split descriptions are present in the full text but would benefit from more explicit reporting of variability across runs. This work is aimed at practitioners who already run or plan federated setups in medical imaging and need a starting rule for choosing an adaptation strategy. A reader looking for deployment advice rather than new theory will get concrete value from the comparison and the code. It deserves a serious referee because the question is relevant and the evaluation is broader than prior piecemeal studies, even if the categorization of shifts needs tightening to make the advice more portable.

Referee Report

2 major / 2 minor

Summary. The paper performs an empirical comparison of data-side harmonization methods versus model-side personalization methods in federated learning for medical imaging. Across six tasks (colon polyp, skin lesion, and breast tumor segmentation; tuberculosis CXR, brain tumor, and breast tumor classification), it reports a conditional trade-off: harmonization outperforms when domain shift is primarily appearance-based (e.g., CXR), personalization is better for structural differences (e.g., polyp segmentation), and the two are comparable when inter-client variation is low. The work supplies practical guidelines and points to hybrid approaches, with code released.

Significance. If the reported ordering holds under a clearer definition of heterogeneity type, the study supplies actionable guidance for federated medical imaging deployments and highlights the value of matching adaptation strategy to shift characteristics rather than applying one paradigm universally. The public code release is a positive contribution that supports reproducibility.

major comments (2)

[Abstract and §4 (Results)] Abstract and §4 (Results): the central claim that the performance ordering is 'driven by the nature of heterogeneity' (appearance-based vs. structural) rests on post-hoc qualitative labels assigned to the six tasks. No section defines or computes a quantitative proxy (e.g., intensity histogram divergence, low-level feature statistics, or shape/semantic variance) that would allow the observed trade-off to be tested on new data or shown to track the claimed driver rather than dataset-specific artifacts or partitioning choices.
[§3 (Experimental Setup) and §4] §3 (Experimental Setup) and §4: the manuscript does not report a measurable criterion or ablation that isolates appearance-based from structural shift, so the conditional guideline cannot be shown to generalize beyond the chosen six settings; the weakest assumption identified in the review (that these tasks sufficiently span real-world domain shifts) therefore remains unaddressed.

minor comments (2)

[§4 and Tables/Figures] Add error bars, statistical significance tests, and explicit data-split / hyperparameter details to all result tables and figures so that the reported performance differences can be independently verified.
[§3] Clarify the exact client partitioning and domain-shift simulation protocol for each of the six datasets so readers can reproduce the heterogeneity levels.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important ways to strengthen the presentation of our empirical findings. We address each major comment below, indicating the revisions we will incorporate in the next version of the manuscript.

read point-by-point responses

Referee: [Abstract and §4 (Results)] Abstract and §4 (Results): the central claim that the performance ordering is 'driven by the nature of heterogeneity' (appearance-based vs. structural) rests on post-hoc qualitative labels assigned to the six tasks. No section defines or computes a quantitative proxy (e.g., intensity histogram divergence, low-level feature statistics, or shape/semantic variance) that would allow the observed trade-off to be tested on new data or shown to track the claimed driver rather than dataset-specific artifacts or partitioning choices.

Authors: We agree that the absence of quantitative proxies leaves the central claim more vulnerable to the interpretation that it reflects dataset idiosyncrasies rather than a general principle. Our qualitative labels were assigned on the basis of established domain knowledge about each task (scanner/protocol-induced intensity variation for CXR versus morphological and lesion-shape differences for polyp segmentation). In the revised manuscript we will add a dedicated paragraph in §4 that reports two simple quantitative proxies computed on the client-wise data distributions: (1) average pairwise Jensen-Shannon divergence of intensity histograms and (2) client-wise variance of low-level texture features (e.g., local binary pattern histograms). These numbers will be tabulated alongside the qualitative labels to provide supporting evidence. We will also add a short discussion of how such metrics could be used prospectively to select an adaptation strategy for a new deployment. This revision directly addresses the post-hoc labeling concern while remaining within the scope of the existing experimental design. revision: yes
Referee: [§3 (Experimental Setup) and §4] §3 (Experimental Setup) and §4: the manuscript does not report a measurable criterion or ablation that isolates appearance-based from structural shift, so the conditional guideline cannot be shown to generalize beyond the chosen six settings; the weakest assumption identified in the review (that these tasks sufficiently span real-world domain shifts) therefore remains unaddressed.

Authors: We concur that controlled ablations isolating appearance-based from structural shifts would provide stronger causal evidence and improve generalizability claims. Designing such ablations without introducing unrealistic artifacts is non-trivial for medical images; therefore our study deliberately used naturally occurring shifts from public datasets. In the revision we will (i) expand the task-selection rationale in §3 with explicit references to the primary shift characteristics documented in the source dataset papers, and (ii) add a limitations subsection that acknowledges the finite coverage of the six tasks and lists concrete directions for future isolating experiments (e.g., style-transfer augmentations for appearance shifts and elastic deformations for structural shifts). These additions clarify the scope of the current guidelines without overstating their universality. revision: partial

Circularity Check

0 steps flagged

Empirical evaluation of existing methods with no derivation or self-referential reduction.

full rationale

The paper performs a comparative study of harmonization and personalization methods across six fixed medical imaging datasets. All claims (conditional trade-off by heterogeneity type) are presented as direct observations from reported performance metrics rather than derived from equations, fitted parameters, or self-citations that reduce to the inputs. No load-bearing step matches any enumerated circularity pattern; the work is self-contained against external benchmarks and does not rename or smuggle prior results as new derivations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on empirical observations from six tasks rather than theoretical derivation. No free parameters or invented entities are described in the abstract. The key premise is a domain assumption about how heterogeneity can be classified.

axioms (1)

domain assumption Domain heterogeneity in federated medical imaging can be usefully classified as primarily appearance-based or structural.
This binary classification is used to interpret which adaptation strategy performs better in each setting.

pith-pipeline@v0.9.0 · 5549 in / 1335 out tokens · 31468 ms · 2026-05-09T20:29:58.525606+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

51 extracted references · 14 canonical work pages · 1 internal anchor

[1]

In: Proc

McMahan, B., Moore, E., Ramage, D., Hampson, S., Aguera y Arcas, B.: Communication- Efficient Learning of Deep Networks from Decentralized Data. In: Proc. of AISTATS, pp. When to Adapt? Adapting the Model or Data in Federated Medical Imaging 9 1273–1282. PMLR, Fort Lauderdale, FL, USA (2017)

2017
[2]

IEEE Access11, 54188–54209 (2023)

Shiranthika, C., Saeedi, P., Bajić, I.V.: Decentralized Learning in Health- care: A Review of Emerging Techniques. IEEE Access11, 54188–54209 (2023). https://doi.org/10.1109/ACCESS.2023.3281832

work page doi:10.1109/access.2023.3281832 2023
[3]

IEEE Access12, 182496–182515 (2024)

Shiranthika, C., Hadizadeh, H., Saeedi, P., Bajić, I.V.: Adaptive Asynchronous Split Fed- erated Learning for Medical Image Segmentation. IEEE Access12, 182496–182515 (2024). https://doi.org/10.1109/ACCESS.2024.3511430

work page doi:10.1109/access.2024.3511430 2024
[4]

Federated Learning with Personalization Layers

Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S.: Federated Learning with Per- sonalization Layers. arXiv preprint arXiv:1912.00818 (2019)

work page internal anchor Pith review arXiv 1912
[5]

In: Proc

Collins, L., Hassani, H., Mokhtari, A., Shakkottai, S.: Exploiting Shared Representations for Personalized Federated Learning. In: Proc. of ICML, Proc. Mach. Learn. Res., vol. 139, pp. 2089–2099. PMLR (2021)

2089
[6]

Fedbn: Federated learning on non-iid features via local batch normalization.arXiv preprint arXiv:2102.07623,

Li, X., Jiang, M., Zhang, X., Kamp, M., Dou, Q.: FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. arXiv preprint arXiv:2102.07623 (2021)

work page arXiv 2021
[7]

Federated meta-learning with fast convergence and efficient communication.arXiv preprint arXiv:1802.07876,

Chen, F., Luo, M., Dong, Z., Li, Z., He, X.: Federated Meta-Learning with Fast Convergence and Efficient Communication. arXiv preprint arXiv:1802.07876 (2018)

work page arXiv 2018
[9]

In: Proc

Fallah, A., Mokhtari, A., Ozdaglar, A.: Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. In: Proc. of NeurIPS, 33, 3557–3568 (2020)

2020
[10]

In: Proc

Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., Zhang, Y.: Personalized Cross-Silo Federated Learning on Non-IID Data. In: Proc. of AAAI 35(9), 7865–7873 (2021)

2021
[11]

In: Proc

Smith, V., Chiang, C.-K., Sanjabi, M., Talwalkar, A.S.: Federated Multi-Task Learning. In: Proc. of NeurIPS, 30 (2017)

2017
[12]

arXiv preprint arXiv:1906.06268 , year=

Corinzia, L., Beuret, A., Buhmann, J.M.: Variational Federated Multi-Task Learning. arXiv preprint arXiv:1906.06268 (2019)

work page arXiv 1906
[13]

In: Proc

Ghosh, A., Chung, J., Yin, D., Ramchandran, K.: An Efficient Framework for Clustered Fed- erated Learning. In: Proc. of NeurIPS, 33, 19586–19597 (2020)

2020
[14]

IEEE BigData 2020, pp

Chen, C., Chen, Z., Zhou, Y., Kailkhura, B.: FedCluster: Boosting the Convergence of Feder- ated Learning via Cluster-Cycling. IEEE BigData 2020, pp. 5017–5026. IEEE (2020)

2020
[15]

Duan, M., Liu, D., Ji, X., Liu, R., Liang, L., Chen, X., Tan, Y.: FedGroup: Efficient Fed- erated Learning via Decomposed Similarity-Based Clustering. In: 2021 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud...

2021
[16]

Fedmd: Heterogenous federated learning via model distillation,

Li, D., Wang, J.: FedMD: Heterogenous Federated Learning via Model Distillation. arXiv preprint arXiv:1910.03581 (2019)

work page arXiv 1910
[17]

IEEE Trans

Zhang, H., Zhu, Y., Wu, T., Cheng, S., Liu, J.: Model-Heterogeneous Federated Learn- ing With Bidirectional Knowledge Distillation. IEEE Trans. Mob. Comput. (2025). https://doi.org/10.1109/TMC.2025.3599315

work page doi:10.1109/tmc.2025.3599315 2025
[18]

In: Proc

Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., McMahan, H.B.: Adaptive Federated Optimization. In: Proc. of ICLR (2021)

2021
[19]

In: Proc

Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated Optimization in Heterogeneous Networks. In: Proc. of MLSys 2, 429–450 (2020)

2020
[20]

In: Proc

Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. In: Proc. of the 37th International Conference on Machine Learning (ICML 2020), Proc. Mach. Learn. Res., vol. 119, pp. 5132–

2020
[21]

In: Proc

Li, T., Hu, S., Beirami, A., Smith, V.: Ditto: Fair and Robust Federated Learning Through Personalization. In: Proc. of ICML 2021, Proc. Mach. Learn. Res., vol. 139, pp. 6357–6368. PMLR (2021)

2021
[22]

Mali, S.A., Ibrahim, A., Woodruff, H.C., Andrearczyk, V., Müller, H., Primakov, S., Salahud- din, Z., Chatterjee, A., Lambin, P.: Making radiomics more reproducible across scanner and imaging protocol variations: A review of harmonization methods. J. Pers. Med. 11(9), 842 (2021)

2021
[23]

Liu, M., Zhu, A.H., Maiti, P., Thomopoulos, S.I., Gadewar, S., Chai, Y., Kim, H., Jahanshad, N., Alzheimer’s Disease Neuroimaging Initiative: Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection. Hum. Brain Mapp. 44(14), 4875–4892 (2023)

2023
[24]

and Zhang, Richard and Zhu, Jun-Yan , title =

Park, T., Efros, A.A., Zhang, R., Zhu, J.-Y.: Contrastive Learning for Unpaired Image- to-Image Translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Com- puter Vision – ECCV 2020, LNCS, vol. 12354, pp. 319–345. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_19

work page doi:10.1007/978-3-030-58545-7_19 2020
[25]

Dewey, B.E., Zhao, C., Reinhold, J.C., Carass, A., Fitzgerald, K.C., Sotirchos, E.S., et al.: DeepHarmony: A deep learning approach to contrast harmonization across scanner changes. Magn. Reson. Imaging, 64, 160–170 (2019). https://doi.org/10.1016/j.mri.2019.05.041 10 C. Shiranthika et al

work page doi:10.1016/j.mri.2019.05.041 2019
[26]

MIA, 88, 102799 (2023)

Cackowski, S., Barbier, E.L., Dojat, M., Christen, T.: Imunity: A generalizable VAE- GAN solution for multicenter MR image harmonization. MIA, 88, 102799 (2023). https://doi.org/10.1016/j.media.2023.102799

work page doi:10.1016/j.media.2023.102799 2023
[27]

In: Proc

Jeong, H., Byun, H., Kang, D.U., Lee, J.: BlindHarmony: "Blind" Harmonization for MR Images via Flow Model. In: Proc. of ICCV, pp. 21129–21139.(2023)

2023
[28]

In: Proc

Huang, X., Belongie, S.: Arbitrary Style Transfer in Real-Time with Adaptive Instance Nor- malization. In: Proc. of ICCV, pp. 1501–1510. IEEE (2017)

2017
[29]

In: Proc

Pizzati, F., Cerri, P., De Charette, F.: CoMoGAN: Continuous Model-Guided Image-to-Image Translation. In: Proc. of CVPR, pp. 14288–14298. IEEE/CVF (2021)

2021
[30]

In: Proc

Hu, X., Zhou, X., Huang, Q., Shi, Z., Sun, L., Li, Q.: QS-Attn: Query-Selected Attention for Contrastive Learning in Image-to-Image Translation. In: Proc. of CVPR, pp. 18291–18300. IEEE/CVF (2022)

2022
[31]

In: Proc

Guo, Z., Guo, D., Zheng, H., Gu, Z., Zheng, B., Dong, J.: Image Harmonization with Trans- former. In: Proc. of ICCV, pp. 14870–14879. (2021)

2021
[32]

In: Proc

Dinh, C.T., Tran, N., Nguyen, J.: Personalized Federated Learning with Moreau Envelopes. In: Proc. of NeurIPS, 33, 21394–21405 (2020)

2020
[33]

In: Proc

Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired Image-to-Image Translation using Cycle- Consistent Adversarial Networks. In: Proc. of ICCV, pp. 2223–2232. IEEE (2017)

2017
[34]

Flower: A friendly federated learning research framework.arXiv preprint arXiv:2007.14390,

Beutel, D.J., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Li, K.H., Parcollet, T., De Gusmão, P.P.B., Lane, N.D.: Flower: A friendly federated learning research framework. arXiv preprint arXiv:2007.14390 (2020)

work page arXiv 2007
[35]

In: Proc

Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., De Lange, T., Johansen, D., Jo- hansen, H.D.: Kvasir-SEG: A segmented polyp dataset. In: Proc. of MMM, LNCS, pp. 451–462. Springer, Cham (2019)

2019
[36]

IJCARS 9(2), 283–293 (2014)

Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. IJCARS 9(2), 283–293 (2014)

2014
[37]

Pattern Recognit 45(9), 3166–3182 (2012)

Bernal, J., Sánchez, J., Vilarino, F.: Towards Automatic Polyp Detection with a Polyp Ap- pearance Model. Pattern Recognit 45(9), 3166–3182 (2012)

2012
[38]

saliency maps from physicians

Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: WM- DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput. Med. Imaging Graph 43, 99–111 (2015)

2015
[39]

Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi- source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 180161 (2018)

2018
[40]

In: Proc

Mendonça, T., Ferreira, P.M., Marques, J., Marcal, A.R.S., Rozeira, J.: PH2 - A dermoscopic image database for research and benchmarking. In: Proc. of IEEE EMBC, Osaka, Japan (2013)

2013
[41]

arXiv preprint arXiv:1703.00523 (2017)

Codella, N.C.F., Nguyen, Q.-B., Pankanti, S., Gutman, D.A., Helba, B., Halpern, A.C., Smith, J.R.: Skin lesion analysis toward melanoma detection: 2017 ISBI challenge. arXiv preprint arXiv:1703.00523 (2017)

work page arXiv 2017
[42]

arXiv preprint arXiv:1807.07391 (2018)

Codella, N.C.F., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S.W., Gutman, D.A., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M.A., Kittler, H., Halpern, A.C.: Skin lesion analysis toward melanoma detection 2018: 2017 ISIC challenge. arXiv preprint arXiv:1807.07391 (2018)

work page arXiv 2018
[43]

Medical Physics 51(4), 3110–3123 (2024)

Gómez-Flores, W., Gregorio-Calas, M.J., Coelho de Albuquerque Pereira, W.: BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems. Medical Physics 51(4), 3110–3123 (2024)

2024
[44]

Mendeley Data, V1 (2017)

Rodrigues, P.S., et al.: BUS_UC: Breast ultrasound dataset. Mendeley Data, V1 (2017). https://data.mendeley.com/datasets/3ksd7w7jkx/1

2017
[45]

Data in Brief 28, 104863 (2020)

Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data in Brief 28, 104863 (2020)

2020
[46]

IEEE JBHI 22(4), 1218–1226 (2018)

Yap, M.H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A.K., Martí, R.: Automated breast ultrasound lesions detection using convolutional neural networks. IEEE JBHI 22(4), 1218–1226 (2018)

2018
[47]

Quantitative Imaging in Medicine and Surgery4, 475–477 (2014)

Jaeger, S., Candemir, S., Antani, S., Wáng, Y.-X.J., Lu, P.-X., Thoma, G.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quantitative Imaging in Medicine and Surgery4, 475–477 (2014)

2014
[48]

In: Proc

Liu, Y., Wu, Y.-H., Ban, Y., Wang, H., Cheng, M.-M.: Rethinking Computer-Aided Tuber- culosis Diagnosis. In: Proc. of CVPR, IEEE/CVF (2020) In: Proc. of CVPR, pp. 2646–2655. IEEE/CVF (2020)

2020
[49]

Kaggle dataset (2020)

Bhuvaji, S.: Brain tumor classification (MRI). Kaggle dataset (2020). https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri

2020
[50]

Kaggle dataset

rm1000: Brain tumor MRI scans. Kaggle dataset. https://www.kaggle.com/datasets/rm1000/brain-tumor-mri-scans
[51]

Kaggle dataset (2020)

Dubail, T.: Brain tumors (256x256). Kaggle dataset (2020). https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256

2020
[52]

PLoS ONE 10(10), e0140381 (2015)

Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., Feng, Q.: Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10), e0140381 (2015)

2015