CosSGD: Nonlinear Quantization for Communication-efficient Federated Learning. - Yang He 005; Maximilian Zenk; Mario Fritz (CoRR, 2020)

The Federated Tumor Segmentation (FeTS) Challenge. - Sarthak Pati; Ujjwal Baid; Maximilian Zenk; Brandon Edwards; Micah J. Sheller; G. Anthony Reina; Patrick Foley; Alexey Gruzdev; Jason Martin; Shadi Albarqouni; Yong Chen 016; Russell Taki Shinohara; et al. (CoRR, 2021)

Realistic Evaluation of FixMatch on Imbalanced Medical Image Classification Tasks. - Maximilian Zenk; David Zimmerer; Fabian Isensee; Paul F. Jäger; Jakob Wasserthal; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2022)

Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana. - Klaus Kades; Jonas Scherer; Maximilian Zenk; Marius Kempf; Klaus H. Maier-Hein (DeCaF/FAIR@MICCAI, 2022)

Federated Learning Enables Big Data for Rare Cancer Boundary Detection. - Sarthak Pati; Ujjwal Baid; Brandon Edwards; Micah J. Sheller; Hans Shih-Han Wang; G. Anthony Reina; Patrick Foley; Alexey Gruzdev; Deepthi Karkada; Christos Davatzikos; Chiharu Sako; Satyam Ghodasara; et al. (CoRR, 2022)

Abstract: Towards Real-world Federated Learning in Medical Image Analysis using Kaapana. - Klaus Kades; Jonas Scherer; Maximilian Zenk; Marius Kempf; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2023)

Why is the Winner the Best? - Matthias Eisenmann; Annika Reinke; Vivienn Weru; Minu Dietlinde Tizabi; Fabian Isensee; Tim J. Adler; Sharib Ali; Vincent Andrearczyk; Marc Aubreville; Ujjwal Baid; Spyridon Bakas; Niranjan Balu; et al. (CVPR, 2023)

MultiTalent: A Multi-dataset Approach to Medical Image Segmentation. - Constantin Ulrich; Fabian Isensee; Tassilo Wald; Maximilian Zenk; Michael Baumgartner 001; Klaus H. Maier-Hein (MICCAI (3), 2023)

Federated benchmarking of medical artificial intelligence with MedPerf. - Alexandros Karargyris; Renato Umeton; Micah J. Sheller; Alejandro Aristizabal; Johnu George; Anna Wuest; Sarthak Pati; Hasan Kassem; Maximilian Zenk; Ujjwal Baid; Prakash Narayana Moorthy; Alexander Chowdhury; et al. (Nat. Mac. Intell., 2023)

Abstract: Multi-dataset Approach to Medical Image Segmentation - MultiTalent. - Constantin Ulrich; Fabian Isensee; Tassilo Wald; Maximilian Zenk; Michael Baumgartner 001; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2024)

Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures. - Yannick Kirchhoff; Maximilian Rokuss; Saikat Roy; Balint Kovacs; Constantin Ulrich; Tassilo Wald; Maximilian Zenk; Philipp Vollmuth; Jens Kleesiek; Fabian Isensee; Klaus H. Maier-Hein (ECCV (77), 2024)

ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation. - Kim-Celine Kahl; Carsten T. Lüth; Maximilian Zenk; Klaus H. Maier-Hein; Paul F. Jaeger (ICLR, 2024)

Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy. - Jessica Kächele; Maximilian Zenk; Maximilian Rokuss; Constantin Ulrich; Tassilo Wald; Klaus H. Maier-Hein (HNTS-MRG@MICCAI, 2024)

Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting. - Maximilian Rokuss; Yannick Kirchhoff; Saikat Roy; Balint Kovacs; Constantin Ulrich; Tassilo Wald; Maximilian Zenk; Stefan Denner; Fabian Isensee; Philipp Vollmuth; Jens Kleesiek; Klaus H. Maier-Hein (LDTM/MMMI/ML4MHD/ML-CDS@MICCAI, 2024)

Mitigating False Predictions in Unreasonable Body Regions. - Constantin Ulrich; Catherine Knobloch; Julius Holzschuh; Tassilo Wald; Maximilian Rokuss; Maximilian Zenk; Maximilian Fischer; Michael Baumgartner 001; Fabian Isensee; Klaus H. Maier-Hein (MLMI@MICCAI (2), 2024)

Abstract: Real-world Federated Learning in Radiology - Hurdles to Overcome and Benefits to Gain. - Markus Bujotzek; Ünal Akünal; Stefan Denner; Peter Neher; Maximilian Zenk; Klaus H. Maier-Hein; Andreas Bucher; Rickmer Braren (Bildverarbeitung für die Medizin, 2025)

Abstract: Skeleton Recall Loss - Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures. - Yannick Kirchhoff; Maximilian Rokuss; Saikat Roy; Balint Kovacs; Constantin Ulrich; Tassilo Wald; Maximilian Zenk; Philipp Vollmuth; Jens Kleesiek; Fabian Isensee; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2025)

Abstract: Client Security Alone Fails in Federated Learning - 2D and 3D Attack Insights. - Santhosh Parampottupadam; Ralf Floca; Dimitrios Bounias; Benjamin Hamm; Saikat Roy; Sinem Sav; Maximilian Zenk; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2025)

Abstract: Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting. - Maximilian Rokuss; Yannick Kirchhoff; Saikat Roy; Balint Kovacs; Constantin Ulrich; Tassilo Wald; Maximilian Zenk; Stefan Denner; Fabian Isensee; Philipp Vollmuth; Jens Kleesiek; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2025)

Real-world federated learning in radiology: hurdles to overcome and benefits to gain. - Markus R. Bujotzek; Ünal Akünal; Stefan Denner; Peter Neher; Maximilian Zenk; Eric Frodl; Astha Jaiswal; Moon S. Kim 002; Nicolai R. Krekiehn; Manuel Nickel; Richard Ruppel; Marcus Both; et al. (J. Am. Medical Informatics Assoc., 2025)

Comparative benchmarking of failure detection methods in medical image segmentation: Unveiling the role of confidence aggregation. - Maximilian Zenk; David Zimmerer; Fabian Isensee; Jeremias Traub; Tobias Norajitra; Paul F. Jäger; Klaus H. Maier-Hein (Medical Image Anal., 2025)

Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge. - Yudi Sang; Yanzhen Liu; Sutuke Yibulayimu; Yunning Wang; Benjamin D. Killeen; Mingxu Liu; Ping-Cheng Ku; Ole Johannsen; Karol Gotkowski; Maximilian Zenk; Klaus H. Maier-Hein; Fabian Isensee; et al. (CoRR, 2025)

Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge. - Vladyslav Zalevskyi; Thomas Sanchez; Misha P. T. Kaandorp; Margaux Roulet; Diego Fajardo Rojas; Liu Li; Jana Hutter; Hongwei Bran Li; Matthew J. Barkovich; Hui Ji; Luca Wilhelmi; Aline Dändliker; et al. (CoRR, 2025)

Inclusive, Differentially Private Federated Learning for Clinical Data. - Santhosh Parampottupadam; Melih Cosgun; Sarthak Pati; Maximilian Zenk; Saikat Roy; Dimitrios Bounias; Benjamin Hamm; Sinem Sav; Ralf Floca; Klaus H. Maier-Hein (CoRR, 2025)