Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge
Language English Country Great Britain, England Media electronic
Document type Journal Article
Grant support
R21 EB030209
NIBIB NIH HHS - United States
R21 CA270742
NCI NIH HHS - United States
U01 CA242871
NCI NIH HHS - United States
U24 CA279629
NCI NIH HHS - United States
P30 CA051008
NCI NIH HHS - United States
R37 CA214955
NCI NIH HHS - United States
Wellcome Trust - United Kingdom
UG3 CA236536
NCI NIH HHS - United States
U24 CA248265
NCI NIH HHS - United States
R01 CA233888
NCI NIH HHS - United States
UL1 TR001433
NCATS NIH HHS - United States
UH3 CA236536
NCI NIH HHS - United States
PubMed
40628696
PubMed Central
PMC12238412
DOI
10.1038/s41467-025-60466-1
PII: 10.1038/s41467-025-60466-1
Knihovny.cz E-resources
- MeSH
- Algorithms * MeSH
- Benchmarking * methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Brain Neoplasms * diagnostic imaging MeSH
- Image Processing, Computer-Assisted * methods MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
Alberta Children's Hospital Research Institute University of Calgary Calgary AB Canada
Alberta Machine Intelligence Institute Edmonton AB Canada
American College of Radiology Reston VA USA
Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA
Biomedical Engineering Program University of Calgary Calgary AB Canada
Brain Imaging and Neuro Epidemiology Group Luxembourg Institute of Health Luxembourg Luxembourg
Brown University Providence RI USA
Case Western Reserve University Cleveland OH USA
Catalan Institute of Oncology Badalona Spain
Center for Federated Learning in Medicine Indiana University Indianapolis IN USA
Center for MR Research University Children's Hospital Zurich Zurich Switzerland
Center for Research and Innovation American College of Radiology Philadelphia PA USA
Centre de recherche du Centre hospitalier universitaire de Sherbrooke Sherbrooke QC Canada
Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic
Changping Laboratory Beijing China
Children's National Hospital Washington DC USA
City St George's University of London London UK
Clínica Imbanaco QuirónSalud Cali Colombia
Clinical Cooperation Unit Neuropathology German Cancer Consortium Heidelberg Germany
Clinical Radiology Laboratory Department of Medicine University of Patras Patras Greece
Clinix Healthcare Lagos Lagos Nigeria
College of Medicine and Public Health Flinders University Bedford Park SA Australia
Columbia University Data Science Institute New York NY USA
Consorci MAR Parc de Salut de Barcelona Catalonia Spain
Department of Bioengineering University of Texas at Dallas Dallas TX USA
Department of Biomedical and Molecular Sciences Queen's University Kingston ON Canada
Department of Biophysics Faculty of Medicine Masaryk University Brno Czech Republic
Department of Chemical Engineering Indian Institute of Technology Kanpur Kanpur Uttar Pradesh India
Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA
Department of Computer Engineering Universidad Carlos 3 de Madrid Madrid Spain
Department of Computer Science and Engineering Beihang University Beijing China
Department of Computer Science and Technology Zhejiang University Hangzhou China
Department of Computer Science Université de Sherbrooke Sherbrooke QC Canada
Department of Computer Science Vanderbilt University Nashville TN USA
Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center Houston TX USA
Department of Electrical and Computer Engineering University of Patras Patras Greece
Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA
Department of Electrical Engineering Qazvin Branch Islamic Azad University Qazvin Iran
Department of Engineering Design IIT Madras Chennai India
Department of Imaging The Clatterbridge Cancer Centre NHS Foundation Trust Liverpool UK
Department of Industrial and Systems Engineering University of Iowa Iowa City IA USA
Department of Informatics Federal University of Parana Curitiba Paraná Brazil
Department of Informatics Technical University of Munich Munich Germany
Department of Mathematics and Computer Science Universitat de Barcelona Barcelona Spain
Department of Mathematics National Taiwan Normal University Taipei Taiwan
Department of Medical Imaging Unity Health Toronto University of Toronto Toronto ON Canada
Department of Neurological Surgery Indiana University School of Medicine Indianapolis IN USA
Department of Neurology Baylor College of Medicine Houston TX USA
Department of Neurooncology Neuromed Campus Kepler University Hospital Linz Linz Austria
Department of Neuropathology Heidelberg University Hospital Heidelberg Germany
Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany
Department of Neuroradiology Klinikum rechts der Isar Munich Germany
Department of Neuroradiology University of Michigan Ann Arbor MI USA
Department of NeuroRadiology University of Patras Patras Greece
Department of Neurosurgery NYU Grossman School of Medicine New York NY USA
Department of Neurosurgery University of Colorado Anschutz Medical Campus Aurora CO USA
Department of Neurosurgery University of Patras Patras Greece
Department of Neurosurgery Vanderbilt University Medical Center Nashville TN USA
Department of Oncology Queen's University Kingston ON Canada
Department of Pathology Memorial Sloan Kettering Cancer Center New York NY USA
Department of Public Health Sciences Henry Ford Health Detroit MI USA
Department of Quantitative Biomedicine University of Zurich Zurich Switzerland
Department of Radiation Oncology Christiana Care Health System Philadelphia PA USA
Department of Radiation Oncology Columbia University Irving Medical Center New York NY USA
Department of Radiation Oncology Henry Ford Health Detroit MI USA
Department of Radiation Oncology Icahn School of Medicine at Mount Sinai New York NY USA
Department of Radiation Oncology James Cancer Center The Ohio State University Columbus OH USA
Department of Radiation Oncology University of Iowa Iowa City IA USA
Department of Radiation Oncology University of Maryland Baltimore MD USA
Department of Radiation Oncology University of Patras Patras Greece
Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston TX USA
Department of Radiology Baylor College of Medicine Houston TX USA
Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA
Department of Radiology Cumming School of Medicine University of Calgary Calgary AB Canada
Department of Radiology Leeds Teaching Hospitals Trust Leeds UK
Department of Radiology Mayo Clinic Rochester MN USA
Department of Radiology Muhammad Abdullahi Wase Teaching Hospital Kano Nigeria
Department of Radiology Neuroradiology Division University of Pittsburgh Pittsburgh PA USA
Department of Radiology NYU Grossman School of Medicine New York NY USA
Department of Radiology Obafemi Awolowo University Ile Ife Ile Ife Osun Nigeria
Department of Radiology Sidney Kimmel Cancer Center Thomas Jefferson University Philadelphia PA USA
Department of Radiology Stanford University Stanford CA USA
Department of Radiology University College Hospital Ibadan Oyo Nigeria
Department of Radiology Washington University in St Louis St Louis MO USA
Department of Radiology Weill Cornell Medicine Cornell University New York NY USA
Division for Computational Radiology and Clinical AI University Hospital Bonn Bonn Germany
École Normale Supérieure Paris France
EPITA Le Kremlin Bicêtre France
Escuela Superior Politecnica del Litoral Guayaquil Guayas Ecuador
Faculty of Arts and Sciences Queen's University Kingston ON Canada
Faculty of Medicine and Health Sciences McGill University Montreal QC Canada
Faculty of Medicine University of Bonn Bonn Germany
Faculty of Science Technology and Medicine University of Luxembourg Esch sur Alzette Luxembourg
Federal Institute of Education Science and Technology of São Paulo Araraquara São Paulo Brazil
Federal University of Parana Curitiba Paraná Brazil
Fujian Normal University Fuzhou China
Fuzhou University Fuzhou China
German Cancer Research Center Heidelberg Division of Intelligent Medical Systems Heidelberg Germany
German Cancer Research Center Heidelberg Division of Medical Image Computing Heidelberg Germany
German Centre for Neurodegenerative Diseases Magdeburg Germany
Graduate School of Informatics Middle East Technical University Ankara Turkey
Graylight Imaging Gliwice Poland
Gustave Roussy Cancer Campus Villejuif France
Helmholtz Imaging German Cancer Research Center Heidelberg Germany
Hotchkiss Brain Institute University of Calgary Calgary AB Canada
Imperial College London London UK
Indian Institute of Information Technology Vadodara Gandhinagar India
Indian Institute of Technology Delhi India
Indiana University Melvin and Bren Simon Comprehensive Cancer Center Indianapolis IN USA
Innovation Center for Biomedical Informatics Georgetown University Washington DC USA
Institució Catalana de Recerca i Estudis Avançats Barcelona Spain
Institute for AI in Medicine University Hospital Essen Essen Germany
Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany
Institute for cognitive neurology and dementia research Magdeburg Germany
Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland
Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan
Institute of Computing University of Campinas Campinas São Paulo Brazil
Institute of High Performance Computing Singapore Singapore
Institute of Neuroradiology Neuromed Campus Kepler University Hospital Linz Linz Austria
Instituto de Neurologia de Curitiba Curitiba Paraná Brazil
Intel Corporation Santa Clara CA USA
ITERM Institute Helmholtz Zentrum Muenchen Neuherberg Germany
Leidos Biomedical Research Inc Frederick National Laboratory for Cancer Research Frederick MD USA
Maria Sklodowska Curie Memorial Cancer Center and Institute of Oncology Gliwice Poland
Mazumdar Shaw Medical Foundation Bengaluru India
Medical College of Wiconsin Milwaukee WI USA
Medical Faculty Heidelberg Heidelberg University Heidelberg Germany
Medical Research Group MLCommons San Francisco CA USA
Monash Biomedical Imaging Monash University Melbourne VIC Australia
Nanjing University of Science and Technology Nanjing China
National Imaging Facility St Lucia QLD Australia
National Taiwan University of Science and Technology Taipei Taiwan
National Tsing Hua University Hsinchu Taiwan
National University of Singapore Yong Loo Lin School of Medicine Singapore Singapore
Neuroimaging Informatics and Analysis Center Washington University in St Louis St Louis MO USA
Neurology Clinic Heidelberg University Hospital Heidelberg Germany
NORLUX Neuro Oncology Laboratory Luxembourg Institute of Health Luxembourg Luxembourg
Novosibirsk State University Novosibirsk Russia
Radiology Department CDI and IDIBAPS Hospital Clinic of Barcelona Barcelona Spain
Riphah International University Islamabad Pakistan
Robert Bosch Center of Data Science and AI IIT Madras Chennai India
Sage Bionetworks Seattle WA USA
School of Automation Northwestern Polytechnical University Xi'an China
School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China
School of Computing Queen's University Kingston ON Canada
School of Data Engineering and AI Technologies Turku University of Applied Sciences Turku Finland
School of Electrical and Computer Engineering Cornell University Ithaca NY USA
School of Electronic Engineering and Computer Science Queen Mary University of London London UK
School of Psychological Sciences Monash University Melbourne VIC Australia
Shanghai Artificial Intelligence Laboratory Shanghai China
Shanghai Jiao Tong University Shanghai China
Sidney Kimmel Medical College Thomas Jefferson University Philadelphia PA USA
Silesian University of Technology Gliwice Poland
Skolkovo Institute of Science and Technology Moscow Russia
Sociedad de Lucha Contral el Cancer SOLCA Guayaquil Ecuador
South Australia Medical Imaging Flinders Medical Centre Bedford Park SA Australia
Symbiosis Center for Medical Image Analysis Symbiosis International University Pune India
The University of Edinburgh Edinburgh UK
Ukrainian Catholic University Lviv Ukraine
Universidad Católica de Cuenca Cuenca Ecuador
Universidad de Concepción Concepción Chile
Universidad del Valle Cali Colombia
Universitas Islam Nahdlatul Ulama Jepara Jepara Indonesia
University of Alabama in Birmingham Birmingham AL USA
University of Alberta Edmonton AB Canada
University of Bergen Bergen Norway
University of Helsinki Helsinki Finland
University of North Carolina at Charlotte Charlotte NC USA
University of Southern California Los Angeles CA USA
University of Texas Southwestern Medical Center Dallas TX USA
University of Virginia Charlottesville VA USA
William S Middleton Memorial Veterans Affairs Madison WI USA
See more in PubMed
Ostrom, Q. T. et al. Cbtrus statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2016–2020. PubMed PMC
Pati, S. et al. Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the ivy glioblastoma atlas project (ivy gap) dataset. PubMed PMC
Baid, U. et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. Preprint at http://arxiv.org/abs/2107.02314 (2021).
Bakas, S. et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the Brats challenge. Preprint at https://arxiv.org/abs/1811.02629 (2018).
Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). PubMed PMC
Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. PubMed PMC
Zech, J. R. et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PubMed PMC
AlBadawy, E. A., Saha, A. & Mazurowski, M. A. Deep learning for segmentation of brain tumors: impact of cross-institutional training and testing. PubMed
Badgeley, M. A. et al. Deep learning predicts hip fracture using confounding patient and healthcare variables. PubMed PMC
Beede, E. et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In
McMahan, B., Moore, E., Ramage, D., Hampson, S. & y Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. In
Pati, S. Privacy preservation for federated learning in health care. PubMed PMC
Kairouz, P. et al. Advances and open problems in federated learning. Preprint at https://arxiv.org/abs/1912.04977 (2019).
Rieke, N. et al. The future of digital health with federated learning. PubMed PMC
Briggs, C., Fan, Z. & Andras, P. Federated learning with hierarchical clustering of local updates to improve training on non-iid data. In
Karimireddy, S. P. et al. Scaffold: stochastic controlled averaging for federated learning. In
Caldas, S. et al. Leaf: a benchmark for federated settings. Preprint at https://arxiv.org/abs/1812.01097 (2018).
du Terrail, J. O. et al. FLamby: datasets and benchmarks for cross-silo federated learning in realistic healthcare settings. Preprint at http://arxiv.org/abs/2210.04620 (2022).
Schmidt, K. et al. Fair evaluation of federated learning algorithms for automated breast density classification: the results of the 2022 acr-nci-nvidia federated learning challenge. PubMed
Maier-Hein, L. et al. Why rankings of biomedical image analysis competitions should be interpreted with care. PubMed PMC
Maier-Hein, L. et al. Bias: transparent reporting of biomedical image analysis challenges. PubMed PMC
Zhou, K., Liu, Z., Qiao, Y., Xiang, T. & Loy, C. C. Domain generalization: a survey. PubMed
Hendrycks, D. & Dietterich, T. Benchmarking neural network robustness to common corruptions and perturbations. Preprint at http://arxiv.org/abs/1903.12261 (2019).
Koh, P. W. et al. WILDS: a benchmark of in-the-wild distribution shifts. Preprint at http://arxiv.org/abs/2012.07421 (2021).
Campello, V. M. et al. Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge. PubMed
Aubreville, M. et al. Mitosis domain generalization in histopathology images – The MIDOG challenge. PubMed
Dayan, I. et al. Federated learning for predicting clinical outcomes in patients with Covid-19. PubMed PMC
Pati, S. et al. Federated learning enables big data for rare cancer boundary detection. PubMed PMC
Ogier du Terrail, J. et al. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. PubMed
Dou, Q. et al. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. PubMed PMC
Karargyris, A. et al. Federated benchmarking of medical artificial intelligence with MedPerf. PubMed PMC
Roth, H. R. et al. Federated learning for breast density classification: a real-world implementation. In
Sheller, M. J. et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. PubMed PMC
Sarma, K. V. et al. Federated learning improves site performance in multicenter deep learning without data sharing. PubMed PMC
Mächler, L., Ezhov, I., Shit, S. & Paetzold, J. C. Fedpidavg: a pid controller inspired aggregation method for federated learning. In
Wang, Y., Kanagavelu, R., Wei, Q., Yang, Y. & Liu, Y. Model aggregation for federated learning considering non-iid and imbalanced data distribution. In
Rawat, A., Zizzo, G., Kadhe, S., Epperlein, J. P. & Braghin, S. Robust learning protocol for federated tumor segmentation challenge. In
Jiang, M., Yang, H., Zhang, X., Zhang, S. & Dou, Q. Efficient federated tumor segmentation via parameter distance weighted aggregation and client pruning. In
Siomos, V., Tarroni, G. & Passerrat-Palmbach, J. Fets challenge 2022 task 1: implementing fedmgda+ and a new partitioning. In
Khan, M. I. et al. Regularized weight aggregation in networked federated learning for glioblastoma segmentation. In
Singh, G. A local score strategy for weight aggregation in federated learning. In
Khan, M. I., Jafaritadi, M., Alhoniemi, E., Kontio, E. & Khan, S. A. Adaptive weight aggregation in federated learning for brain tumor segmentation. In
Yin, Y. et al. Efficient federated tumor segmentation via normalized tensor aggregation and client pruning. In
Mächler, L. et al. Fedcostwavg: A new averaging for better federated learning. In
Linardos, A., Kushibar, K. & Lekadir, K. Center dropout: a simple method for speed and fairness in federated learning. In
Tuladhar, A., Tyagi, L., Souza, R. & Forkert, N. D. Federated learning using variable local training for brain tumor segmentation. In
Souza, R. et al. Multi-institutional travelling model for tumor segmentation in mri datasets. In
Shambhat, V. et al. A study on criteria for training collaborator selection in federated learning. In
Isik-Polat, E., Polat, G., Kocyigit, A. & Temizel, A. Evaluation and analysis of different aggregation and hyperparameter selection methods for federated brain tumor segmentation. In
Reddi, S. et al. Adaptive federated optimization. Preprint at http://arxiv.org/abs/2003.00295 (2020).
Wen, P. Y. et al. Rano 2.0: update to the response assessment in neuro-oncology criteria for high-and low-grade gliomas in adults. PubMed PMC
Bakas, S. et al. Advancing the Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. PubMed PMC
Bakas, S. et al. Segmentation labels for the pre-operative scans of the TCGA-GBM collection.
Bakas, S. et al. Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection.
Rohlfing, T., Zahr, N. M., Sullivan, E. V. & Pfefferbaum, A. The sri24 multichannel atlas of normal adult human brain structure. PubMed PMC
Yushkevich, P. A. et al. Fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 Tesla and 7 Tesla T2-weighted MRI.
Thakur, S. et al. Brain extraction on MRI scans in presence of diffuse glioma: multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. PubMed PMC
Davatzikos, C. et al. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. PubMed PMC
Pati, S. et al. The cancer imaging phenomics toolkit (CAPTK): technical overview. In PubMed PMC
Rathore, S. et al. Brain cancer imaging phenomics toolkit (brain-captk): an interactive platform for quantitative analysis of glioblastoma. In PubMed PMC
Pati, S. et al. The federated tumor segmentation (FETS) tool: an open-source solution to further solid tumor research. PubMed PMC
Kamnitsas, K. et al. Efficient multi-scale 3d cnn with fully connected CRF for accurate brain lesion segmentation. PubMed
Isensee, F., Jäger, P. F., Full, P. M., Vollmuth, P. & Maier-Hein, K. H. nnu-net for brain tumor segmentation. In
McKinley, R., Meier, R. & Wiest, R. Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In
Warfield, S. K., Zou, K. H. & Wells, W. M. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. PubMed PMC
Pati, S. Fets-ai/labelfusion: Sdist added to pypi. 10.5281/zenodo.4633206 (2021).
Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. In
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S. & Pal, C. The importance of skip connections in biomedical image segmentation. In
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3d U-Net: learning dense volumetric segmentation from sparse annotation. In
Pati, S. et al. GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows.
Sheller, M. J., Reina, G. A., Edwards, B., Martin, J. & Bakas, S. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In PubMed PMC
Paszke, A. et al. Pytorch: an imperative style, high-performance deep learning library.
Foley, P. et al. Openfl: the open federated learning library. PubMed DOI PMC
Gulrajani, I. & Lopez-Paz, D. In search of lost domain generalization. In
Merkel, D. Docker: lightweight Linux containers for consistent development and deployment.
Kurtzer, G. M., Sochat, V. & Bauer, M. W. Singularity: scientific containers for mobility of compute. PubMed PMC
Hu, Z., Shaloudegi, K., Zhang, G. & Yu, Y. Federated learning meets multi-objective optimization.
Kotowski, K. et al. Federated evaluation of nnu-nets enhanced with domain knowledge for brain tumor segmentation. In
Shi, Y., Gao, H., Avestimehr, S. & Yan, Y. Experimenting fedml and nvflare for federated tumor segmentation challenge. In
Ren, J. et al. Ensemble outperforms single models in brain tumor segmentation. In
Kingma, D. P. Adam: A method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Yeganeh, Y., Farshad, A., Navab, N. & Albarqouni, S. Inverse distance aggregation for federated learning with non-iid data. In
Peiris, H., Hayat, M., Chen, Z., Egan, G. & Harandi, M. Hybrid window attention based transformer architecture for brain tumor segmentation. In
Liu, Z. et al. Swin transformer: hierarchical vision transformer using shifted windows. In
Dong, X. et al. Cswin transformer: a general vision transformer backbone with cross-shaped windows-2022 IEEE. In
Sun, K. et al. High-resolution representations for labeling pixels and regions. Preprint at https://arxiv.org/abs/1904.04514 (2019).
Jia, H., Bai, C., Cai, W., Huang, H. & Xia, Y. Hnf-netv2 for brain tumor segmentation using multi-modal MR imaging. In
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N. & Liang, J. Unet++: a nested U-Net architecture for medical image segmentation. In PubMed PMC
Chao, P., Kao, C.-Y., Ruan, Y.-S., Huang, C.-H. & Lin, Y.-L. Hardnet: a low memory traffic network. In
Xie, Y., Zhang, J., Shen, C. & Xia, Y. Cotr: Efficiently bridging CNN and transformer for 3d medical image segmentation. In
Fidon, L. et al. Generalized wasserstein dice loss, test-time augmentation, and transformers for the brats 2021 challenge. In
Miyato, T., Maeda, S.-i, Koyama, M. & Ishii, S. Virtual adversarial training: a regularization method for supervised and semi-supervised learning. PubMed
Zenk, M. et al. Fets-ai/challenge: creating a new release after incorporating all analysis code 10.5281/zenodo.15102249 (2025).