{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:22:30Z","timestamp":1788182550772,"version":"build-2803163510"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004937","name":"Bundesministerium f\u00fcr Forschung und Technologie","doi-asserted-by":"publisher","award":["01KX2021"],"award-info":[{"award-number":["01KX2021"]}],"id":[{"id":"10.13039\/501100004937","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004937","name":"Bundesministerium f\u00fcr Forschung und Technologie","doi-asserted-by":"publisher","award":["01ZZ2316M"],"award-info":[{"award-number":["01ZZ2316M"]}],"id":[{"id":"10.13039\/501100004937","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004937","name":"Bundesministerium f\u00fcr Forschung und Technologie","doi-asserted-by":"publisher","award":["13GW0388A"],"award-info":[{"award-number":["13GW0388A"]}],"id":[{"id":"10.13039\/501100004937","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels. Federated noisy label learning (FNLL) aims to mitigate these effects, yet remains underused in practice as existing evidence is largely based on synthetic noise, simplified settings, and limited real-world noisy evaluation. We address this gap by introducing a benchmark suite that combines diverse real-world noisy datasets, deployment-relevant client-noise scenarios, and label-noise-targeted evaluation to support systematic FNLL assessment and informed method selection. The suite combines curated real-world noisy medical image segmentation datasets from diverse sources with a comprehensive federated segmentation framework including various client-noise scenarios and noise-targeted evaluation. To demonstrate its capabilities, we compare representative FNLL methods across approaches, including noise-aware aggregation, robust personalization, label correction, and sample selection. In-depth data analysis shows that real-world segmentation label noise occurs both in isolation and in combination of characterized noise types. The benchmark shows that\n                    <jats:italic>FedSelect<\/jats:italic>\n                    performs strongest within noise-targeted evaluation, underlines\n                    <jats:italic>FedAvg<\/jats:italic>\n                    as a competitive baseline, and provides a practically informed decision guide for FNLL method selection based on label-noise type, client-noise scenario, and technical-cost considerations. The presented suite provides a realistic and discriminative basis for FNLL evaluation in medical image segmentation and establishes a reusable foundation for fair benchmarking, dataset-specific label-noise characterization, and future method development under realistic federated settings. Code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/MIC-DKFZ\/FedSegNoiseBench\" ext-link-type=\"uri\">https:\/\/github.com\/MIC-DKFZ\/FedSegNoiseBench<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s10278-026-02205-6","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:08:11Z","timestamp":1788181691000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4550-428X","authenticated-orcid":false,"given":"Markus","family":"Bujotzek","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Bounias","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Denner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralf","family":"Floca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Fischer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Neher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Maier-Hein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"2205_CR1","doi-asserted-by":"crossref","unstructured":"Rieke N., Hancox J., Li W., Milletari F., Roth H.R., Albarqouni S., Bakas S., Galtier M.N., Landman B.A., Maier-Hein K., et al.: The future of digital health with federated learning. NPJ digital medicine 3(1), 119 (2020)","DOI":"10.1038\/s41746-020-00323-1"},{"key":"2205_CR2","doi-asserted-by":"crossref","unstructured":"Sheller M.J., Edwards B., Reina G.A., Martin J., Pati S., Kotrotsou A., Milchenko M., Xu W., Marcus D., Colen R.R., Bakas S.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Reports 10 (2020)","DOI":"10.1038\/s41598-020-69250-1"},{"key":"2205_CR3","unstructured":"Pati S., Baid U., Edwards B., Sheller M., Wang S.-H., Reina G.A., Foley P., Gruzdev A., Karkada D., Davatzikos C., et al.: Federated learning enables big data for rare cancer boundary detection. Nature communications 13(1), 7346 (2022)"},{"key":"2205_CR4","doi-asserted-by":"crossref","unstructured":"Bujotzek M.R., Ak\u00fcnal \u00dc., Denner S., Neher P., Zenk M., Frodl E., Jaiswal A., Kim M., Krekiehn N.R., Nickel M., et al.: Real-world federated learning in radiology: hurdles to overcome and benefits to gain. Journal of the American Medical Informatics Association 32(1), 193\u2013205 (2025)","DOI":"10.1093\/jamia\/ocae259"},{"key":"2205_CR5","doi-asserted-by":"crossref","unstructured":"Oldenhof M., \u00c1cs G., Pej\u00f3 B., Schuffenhauer A., Holway N., Sturm N., Dieckmann A., Fortmeier O., Boniface E., Mayer C., et al.: Industry-scale orchestrated federated learning for drug discovery. In: Proceedings of the Aaai Conference on Artificial Intelligence, vol. 37, pp. 15576\u201315584 (2023)","DOI":"10.1609\/aaai.v37i13.26847"},{"key":"2205_CR6","doi-asserted-by":"crossref","unstructured":"Antonelli M., Reinke A., Bakas S., Farahani K., Kopp-Schneider A., Landman B.A., Litjens G., Menze B., Ronneberger O., Summers R.M., et al.: The medical segmentation decathlon. Nature communications 13(1), 4128 (2022)","DOI":"10.1038\/s41467-022-30695-9"},{"key":"2205_CR7","doi-asserted-by":"crossref","unstructured":"Karimi D., Dou H., Warfield S.K., Gholipour A.: Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. Medical image analysis 65, 101759 (2020)","DOI":"10.1016\/j.media.2020.101759"},{"key":"2205_CR8","doi-asserted-by":"crossref","unstructured":"Aliotta E., Nourzadeh H., Siebers J.: Quantifying the dosimetric impact of organ-at-risk delineation variability in head and neck radiation therapy in the context of patient setup uncertainty. Physics in Medicine & Biology 64(13), 135020 (2019)","DOI":"10.1088\/1361-6560\/ab205c"},{"key":"2205_CR9","doi-asserted-by":"crossref","unstructured":"Poel R., R\u00fcfenacht E., Ermis E., M\u00fcller M., Fix M.K., Aebersold D.M., Manser P., Reyes M.: Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma. Radiation Oncology 17(1), 170 (2022)","DOI":"10.1186\/s13014-022-02137-9"},{"key":"2205_CR10","doi-asserted-by":"crossref","unstructured":"Ma J., He Y., Li F., Han L., You C., Wang B.: Segment anything in medical images. Nature communications 15(1), 654 (2024)","DOI":"10.1038\/s41467-024-44824-z"},{"key":"2205_CR11","doi-asserted-by":"crossref","unstructured":"Fang X., Ye M.: Robust federated learning with noisy and heterogeneous clients. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10072\u201310081 (2022)","DOI":"10.1109\/CVPR52688.2022.00983"},{"key":"2205_CR12","doi-asserted-by":"crossref","unstructured":"Armato\u00a0III S.G., McLennan G., Bidaut L., McNitt-Gray M.F., Meyer C.R., Reeves A.P., Zhao B., Aberle D.R., Henschke C.I., Hoffman E.A., et al.: The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Medical physics 38(2), 915\u2013931 (2011)","DOI":"10.1118\/1.3528204"},{"key":"2205_CR13","doi-asserted-by":"crossref","unstructured":"Liao Z., Hu S., Xie Y., Xia Y.: Modeling annotator preference and stochastic annotation error for medical image segmentation. Medical Image Analysis 92, 103028 (2024)","DOI":"10.1016\/j.media.2023.103028"},{"key":"2205_CR14","doi-asserted-by":"crossref","unstructured":"Zhong Q., Guo T., Rechsteiner M., R\u00fcschoff J.H., Rupp N., Fankhauser C., Saba K., Mortezavi A., Poyet C., Hermanns T., et al.: A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients. Scientific data 4(1), 170014 (2017)","DOI":"10.1038\/sdata.2017.14"},{"key":"2205_CR15","doi-asserted-by":"crossref","unstructured":"Jensen M., Clemmensen A., Hansen J.G., Krimpen\u00a0Mortensen J., Christensen E.N., Kjaer A., Ripa R.S.: 3d whole body preclinical micro-ct database of subcutaneous tumors in mice with annotations from 3 annotators. Scientific Data 11(1), 1021 (2024)","DOI":"10.1038\/s41597-024-03814-y"},{"key":"2205_CR16","doi-asserted-by":"crossref","unstructured":"Wu Y., Xie Y., Luo X., Wu Q., Cai J.: Dataset, challenge, and evaluation for tumor segmentation variability. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 11302\u201311303 (2024)","DOI":"10.1145\/3664647.3689150"},{"key":"2205_CR17","doi-asserted-by":"publisher","unstructured":"Garrucho L., Kushibar K., Reidel C.-A., Joshi S., Osuala R., Tsirikoglou A., Bobowicz M., Riego J.d., Catanese A., Gwo\u017adziewicz K., Cosaka M.-L., Abo-Elhoda P.M., Tantawy S.W., Sakrana S.S., Shawky-Abdelfatah N.O., Salem A.M.A., Kozana A., Divjak E., Ivanac G., Nikiforaki K., Klontzas M.E., Garc\u00eda-Dosd\u00e1 R., Gulsun-Akpinar M., Lafc\u0131 O., Mann R., Mart\u00edn-Isla C., Prior F., Marias K., Starmans M.P.A., Strand F., D\u00edaz O., Igual L., Lekadir K.: A large-scale multicenter breast cancer dce-mri benchmark dataset with expert segmentations. Scientific Data 12(1), 453 (2025) https:\/\/doi.org\/10.1038\/s41597-025-04707-4","DOI":"10.1038\/s41597-025-04707-4"},{"key":"2205_CR18","doi-asserted-by":"crossref","unstructured":"Shi J., Zhang K., Guo C., Yang Y., Xu Y., Wu J.: A survey of label-noise deep learning for medical image analysis. Medical image analysis 95, 103166 (2024)","DOI":"10.1016\/j.media.2024.103166"},{"key":"2205_CR19","doi-asserted-by":"crossref","unstructured":"Wei Y., Deng Y., Sun C., Lin M., Jiang H., Peng Y.: Deep learning with noisy labels in medical prediction problems: a scoping review. Journal of the American Medical Informatics Association 31(7), 1596\u20131607 (2024)","DOI":"10.1093\/jamia\/ocae108"},{"key":"2205_CR20","doi-asserted-by":"crossref","unstructured":"Song H., Kim M., Park D., Shin Y., Lee J.-G.: Learning from noisy labels with deep neural networks: A survey. IEEE transactions on neural networks and learning systems 34(11), 8135\u20138153 (2022)","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"2205_CR21","doi-asserted-by":"crossref","unstructured":"Dgani Y., Greenspan H., Goldberger J.: Training a neural network based on unreliable human annotation of medical images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 39\u201342 (2018). IEEE","DOI":"10.1109\/ISBI.2018.8363518"},{"key":"2205_CR22","doi-asserted-by":"crossref","unstructured":"Hu K., Huang Y., Huang W., Tan H., Chen Z., Zhong Z., Li X., Zhang Y., Gao X.: Deep supervised learning using self-adaptive auxiliary loss for covid-19 diagnosis from imbalanced ct images. Neurocomputing 458, 232\u2013245 (2021)","DOI":"10.1016\/j.neucom.2021.06.012"},{"key":"2205_CR23","doi-asserted-by":"crossref","unstructured":"Chen H., Tan W., Li J., Guan P., Wu L., Yan B., Li J., Wang Y.: Adaptive cross entropy for ultrasmall object detection in computed tomography with noisy labels. Computers in Biology and Medicine 147, 105763 (2022)","DOI":"10.1016\/j.compbiomed.2022.105763"},{"key":"2205_CR24","doi-asserted-by":"crossref","unstructured":"Pham H.H., Le T.T., Tran D.Q., Ngo D.T., Nguyen H.Q.: Interpreting chest x-rays via cnns that exploit hierarchical disease dependencies and uncertainty labels. Neurocomputing 437, 186\u2013194 (2021)","DOI":"10.1016\/j.neucom.2020.03.127"},{"key":"2205_CR25","unstructured":"Qian C., Han K., Ding J., Lyu C., Yuan Z., Chen J., Liu Z.: Adaptive label correction for robust medical image segmentation with noisy labels. arXiv preprint arXiv:2503.12218 (2025)"},{"key":"2205_CR26","unstructured":"Zhou T., Wang S., Bilmes J.: Robust curriculum learning: from clean label detection to noisy label self-correction. In: International Conference on Learning Representations (2020)"},{"key":"2205_CR27","doi-asserted-by":"crossref","unstructured":"Li P., Purkait P., Ajanthan T., Abdolshah M., Garg R., Husain H., Xu C., Gould S., Ouyang W., Van Den\u00a0Hengel A.: Semi-supervised semantic segmentation under label noise via diverse learning groups. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1229\u20131238 (2023)","DOI":"10.1109\/ICCV51070.2023.00119"},{"key":"2205_CR28","doi-asserted-by":"crossref","unstructured":"Zheng G., Awadallah A.H., Dumais S.: Meta label correction for noisy label learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11053\u201311061 (2021)","DOI":"10.1609\/aaai.v35i12.17319"},{"key":"2205_CR29","doi-asserted-by":"crossref","unstructured":"Huang B., Lin Y., Xu C.: Contrastive label correction for noisy label learning. Information sciences 611, 173\u2013184 (2022)","DOI":"10.1016\/j.ins.2022.08.060"},{"key":"2205_CR30","doi-asserted-by":"crossref","unstructured":"Ma F., Wu Y., Yu X., Yang Y.: Learning with noisy labels via self-reweighting from class centroids. IEEE Transactions on Neural Networks and Learning Systems 33(11), 6275\u20136285 (2021)","DOI":"10.1109\/TNNLS.2021.3073248"},{"key":"2205_CR31","doi-asserted-by":"crossref","unstructured":"Li J., Song Y., Zhu J., Cheng L., Su Y., Ye L., Yuan P., Han S.: Learning from large-scale noisy web data with ubiquitous reweighting for image classification. IEEE transactions on pattern analysis and machine intelligence 43(5), 1808\u20131814 (2019)","DOI":"10.1109\/TPAMI.2019.2961910"},{"key":"2205_CR32","doi-asserted-by":"crossref","unstructured":"Mirikharaji Z., Yan Y., Hamarneh G.: Learning to segment skin lesions from noisy annotations. In: MICCAI Workshop on Domain Adaptation and Representation Transfer, pp. 207\u2013215 (2019). Springer","DOI":"10.1007\/978-3-030-33391-1_24"},{"key":"2205_CR33","unstructured":"Xiao L., Li Y., Qv L., Tian X., Peng Y., Zhou S.K.: Pathological image segmentation with noisy labels. arXiv preprint arXiv:2104.02602 (2021)"},{"key":"2205_CR34","unstructured":"Mindermann S., Brauner J.M., Razzak M.T., Sharma M., Kirsch A., Xu W., H\u00f6ltgen B., Gomez A.N., Morisot A., Farquhar S., et al.: Prioritized training on points that are learnable, worth learning, and not yet learnt. In: International Conference on Machine Learning, pp. 15630\u201315649 (2022). PMLR"},{"key":"2205_CR35","unstructured":"Xia X., Liu T., Han B., Gong M., Yu J., Niu G., Sugiyama M.: Sample selection with uncertainty of losses for learning with noisy labels. arXiv preprint arXiv:2106.00445 (2021)"},{"key":"2205_CR36","unstructured":"Kim T., Ko J., Choi J., Yun S.-Y., et al.: Fine samples for learning with noisy labels. Advances in Neural Information Processing Systems 34, 24137\u201324149 (2021)"},{"key":"2205_CR37","doi-asserted-by":"crossref","unstructured":"Ribeiro V., Avila S., Valle E.: Less is more: Sample selection and label conditioning improve skin lesion segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 738\u2013739 (2020)","DOI":"10.1109\/CVPRW50498.2020.00377"},{"key":"2205_CR38","doi-asserted-by":"crossref","unstructured":"Xu J., Chen Z., Quek T.Q., Chong K.F.E.: Fedcorr: Multi-stage federated learning for label noise correction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10184\u201310193 (2022)","DOI":"10.1109\/CVPR52688.2022.00994"},{"key":"2205_CR39","doi-asserted-by":"crossref","unstructured":"Zhang Y., Miao X., Li B., Wu Y., Shang Y.: Proxy-validated importance-aware federated sample selection with meta learning. In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, pp. 3855\u20133866 (2025)","DOI":"10.1145\/3711896.3737093"},{"key":"2205_CR40","doi-asserted-by":"crossref","unstructured":"Wu N., Yu L., Jiang X., Cheng K.-T., Yan Z.: Fednoro: Towards noise-robust federated learning by addressing class imbalance and label noise heterogeneity. arXiv preprint arXiv:2305.05230 (2023)","DOI":"10.24963\/ijcai.2023\/492"},{"key":"2205_CR41","doi-asserted-by":"crossref","unstructured":"Jiang X., Sun S., Li J., Xue J., Li R., Wu Z., Xu G., Wang Y., Liu M.: Tackling noisy clients in federated learning with end-to-end label correction. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 1015\u20131026 (2024)","DOI":"10.1145\/3627673.3679550"},{"key":"2205_CR42","doi-asserted-by":"crossref","unstructured":"Chen Z., Li W., Xing X., Yuan Y.: Medical federated learning with joint graph purification for noisy label learning. Medical Image Analysis 90, 102976 (2023)","DOI":"10.1016\/j.media.2023.102976"},{"key":"2205_CR43","doi-asserted-by":"crossref","unstructured":"Wu N., Sun Z., Yan Z., Yu L.: Feda3i: annotation quality-aware aggregation for federated medical image segmentation against heterogeneous annotation noise. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 15943\u201315951 (2024)","DOI":"10.1609\/aaai.v38i14.29525"},{"key":"2205_CR44","doi-asserted-by":"crossref","unstructured":"Xiang Y., Wu N., Yu L., Yang X., Cheng K.-T., Yan Z.: Fedia: Federated medical image segmentation with heterogeneous annotation completeness. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 373\u2013382 (2024). Springer","DOI":"10.1007\/978-3-031-72117-5_35"},{"key":"2205_CR45","doi-asserted-by":"crossref","unstructured":"Zhu M., Chen Z., Yuan Y.: Feddm: Federated weakly supervised segmentation via annotation calibration and gradient de-conflicting. IEEE Transactions on Medical Imaging 42(6), 1632\u20131643 (2023)","DOI":"10.1109\/TMI.2023.3235757"},{"key":"2205_CR46","doi-asserted-by":"crossref","unstructured":"Wang Y., Ding W., Lin W., Tan T., Gao Z.: Fedhnr: Federated hierarchical resilient learning for echocardiogram segmentation with annotation noise. Expert Systems with Applications 273, 126841 (2025)","DOI":"10.1016\/j.eswa.2025.126841"},{"key":"2205_CR47","doi-asserted-by":"crossref","unstructured":"Wang J., Jin Y., Stoyanov D., Wang L.: Feddp: Dual personalization in federated medical image segmentation. IEEE Transactions on Medical Imaging 43(1), 297\u2013308 (2023)","DOI":"10.1109\/TMI.2023.3299206"},{"key":"2205_CR48","doi-asserted-by":"crossref","unstructured":"Jiang M., Yang H., Cheng C., Dou Q.: Iop-fl: Inside-outside personalization for federated medical image segmentation. IEEE Transactions on Medical Imaging 42(7), 2106\u20132117 (2023)","DOI":"10.1109\/TMI.2023.3263072"},{"key":"2205_CR49","doi-asserted-by":"crossref","unstructured":"Yang S., Park H., Byun J., Kim C.: Robust federated learning with noisy labels. IEEE Intelligent Systems 37(2), 35\u201343 (2022)","DOI":"10.1109\/MIS.2022.3151466"},{"key":"2205_CR50","doi-asserted-by":"crossref","unstructured":"Bai L., Wang D., Wang H., Barnett M., Cabezas M., Cai W., Calamante F., Kyle K., Liu D., Ly L., et al.: Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training. Artificial Intelligence in Medicine 152, 102872 (2024)","DOI":"10.1016\/j.artmed.2024.102872"},{"key":"2205_CR51","doi-asserted-by":"crossref","unstructured":"Shin J., Li Y., Liu Y., Lee S.-J.: Fedbalancer: Data and pace control for efficient federated learning on heterogeneous clients. In: Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services, pp. 436\u2013449 (2022)","DOI":"10.1145\/3498361.3538917"},{"key":"2205_CR52","doi-asserted-by":"crossref","unstructured":"Ma Y., Hou J., Zhang C., Zhou Y., Ge Z., Xie H., Ju L.: Benchmarking real-world medical image classification with noisy labels: Challenges, practice, and outlook. Pattern Recognition, 113647 (2026)","DOI":"10.1016\/j.patcog.2026.113647"},{"key":"2205_CR53","unstructured":"Moradi A., Abrahamsen B.S., Geerdink J., Yakar D., Huisman H., Bathen T.F., Elschot M.: Beyond the sandbox: Real-world federated learning for mri prostate cancer detection (2025)"},{"key":"2205_CR54","doi-asserted-by":"crossref","unstructured":"Dalva Y., Pehlivan H., Alt\u0131ndi\u015f S.F., Dundar A.: Benchmarking the robustness of instance segmentation models. IEEE Transactions on Neural Networks and Learning Systems 35(12), 17021\u201317035 (2023)","DOI":"10.1109\/TNNLS.2023.3310985"},{"key":"2205_CR55","doi-asserted-by":"crossref","unstructured":"Jiang X., Li J., Wu N., Wu Z., Li X., Sun S., Xu G., Wang Y., Li Q., Liu M.: Fnbench: Benchmarking robust federated learning against noisy labels. arXiv preprint arXiv:2505.06684 (2025)","DOI":"10.36227\/techrxiv.172503083.36644691\/v1"},{"key":"2205_CR56","unstructured":"Liang S., Huang J., Hong J., Zeng D., Zhou J., Xu Z.: Fednoisy: Federated noisy label learning benchmark. arXiv preprint arXiv:2306.11650 (2023)"},{"key":"2205_CR57","unstructured":"Caldas S., Duddu S.M.K., Wu P., Li T., Kone\u010dn\u1ef3 J., McMahan H.B., Smith V., Talwalkar A.: Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097 (2018)"},{"key":"2205_CR58","doi-asserted-by":"crossref","unstructured":"Terrail J., Ayed S.-S., Cyffers E., Grimberg F., He C., Loeb R., Mangold P., Marchand T., Marfoq O., Mushtaq E., et al.: Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings. Advances in Neural Information Processing Systems 35, 5315\u20135334 (2022)","DOI":"10.52202\/068431-0384"},{"key":"2205_CR59","doi-asserted-by":"crossref","unstructured":"Karargyris A., Umeton R., Sheller M.J., Aristizabal A., George J., Wuest A., Pati S., Kassem H., Zenk M., Baid U., et al.: Federated benchmarking of medical artificial intelligence with medperf. Nature machine intelligence 5(7), 799\u2013810 (2023)","DOI":"10.1038\/s42256-023-00652-2"},{"key":"2205_CR60","doi-asserted-by":"crossref","unstructured":"Isensee F., Jaeger P.F., Kohl S.A., Petersen J., Maier-Hein K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18(2), 203\u2013211 (2021)","DOI":"10.1038\/s41592-020-01008-z"},{"key":"2205_CR61","doi-asserted-by":"crossref","unstructured":"Isensee F., Wald T., Ulrich C., Baumgartner M., Roy S., Maier-Hein K., Jaeger P.F.: nnu-net revisited: A call for rigorous validation in 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 488\u2013498 (2024). Springer","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"2205_CR62","doi-asserted-by":"crossref","unstructured":"Warfield S.K., Zou K.H., Wells W.M.: Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. IEEE transactions on medical imaging 23(7), 903\u2013921 (2004)","DOI":"10.1109\/TMI.2004.828354"},{"key":"2205_CR63","doi-asserted-by":"crossref","unstructured":"Almazroa A., Alodhayb S., Osman E., Ramadan E., Hummadi M., Dlaim M., Alkatee M., Raahemifar K., Lakshminarayanan V.: Retinal fundus images for glaucoma analysis: the riga dataset. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, vol. 10579, pp. 55\u201362 (2018). SPIE","DOI":"10.1117\/12.2293584"},{"key":"2205_CR64","doi-asserted-by":"publisher","unstructured":"Arvaniti E., Fricker K., Moret M., Rupp N., Hermanns T., Fankhauser C., Wey N., Wild P., R\u00fcschoff J.H., Claassen M.: Replication Data for: Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Harvard Dataverse (2018). https:\/\/doi.org\/10.7910\/DVN\/OCYCMP","DOI":"10.7910\/DVN\/OCYCMP"},{"key":"2205_CR65","doi-asserted-by":"crossref","unstructured":"Mittmann G., Laiouar-Pedari S., Mehrtens H.A., Haggenm\u00fcller S., Bucher T.-C., Chanda T., Gaisa N.T., Wagner M., Klamminger G.G., Rau T.T., et al.: Pathologist-like explainable ai for interpretable gleason grading in prostate cancer. Nature communications 16(1), 8959 (2025)","DOI":"10.1038\/s41467-025-64712-4"},{"key":"2205_CR66","doi-asserted-by":"crossref","unstructured":"Kades K., Scherer J., Zenk M., Kempf M., Maier-Hein K.: Towards real-world federated learning in medical image analysis using kaapana. In: International Workshop on Distributed, Collaborative, and Federated Learning, pp. 130\u2013140 (2022). Springer","DOI":"10.1007\/978-3-031-18523-6_13"},{"key":"2205_CR67","unstructured":"McMahan B., Moore E., Ramage D., Hampson S., Arcas B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017). Pmlr"},{"key":"2205_CR68","doi-asserted-by":"crossref","unstructured":"Tuor T., Wang S., Ko B.J., Liu C., Leung K.K.: Overcoming noisy and irrelevant data in federated learning. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 5020\u20135027 (2021). IEEE","DOI":"10.1109\/ICPR48806.2021.9412599"},{"key":"2205_CR69","unstructured":"Grad E., Kimhi M., Halika L., Baskin C.: Benchmarking label noise in instance segmentation: Spatial noise matters. arXiv e-prints, 2406 (2024)"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02205-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02205-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02205-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:08:41Z","timestamp":1788181721000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02205-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":69,"alternative-id":["2205"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02205-6","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]},"assertion":[{"value":"27 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study exclusively used publicly available datasets. All data were collected and made available by the original studies in accordance with relevant ethical guidelines and approvals. No additional ethical approval was required for this work.","order":1,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. This study used only publicly available, de-identified data.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. This study did not involve any identifiable individual data.","order":3,"name":"Ethics","label":"Consent to Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}