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1st BRIDGE/ 6th DeCaF@MICCAI 2025: Daejeon, South Korea
- Ghada Zamzmi
, Annika Reinke
Bridging Regulatory Science and Medical Imaging Evaluation; and Distributed, Collaborative, and Federated Learning - First International Workshop, BRIDGE 2025, and 6th International Workshop, DeCaF 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23 and September 27, 2025, Proceedings. Lecture Notes in Computer Science 16135, Springer 2026, ISBN 978-3-032-05665-8
Proceedings of the MICCAI Workshop on Bridging Regulatory Science and Medical AI (BRIDGE 2025)
- Vasiliki Sideri-Lampretsa, Daniel Rueckert, Huaqi Qiu:
Evaluation of Deformable Image Registration Under Alignment-Regularity Trade-Off. 5-14 - Pascaline Andrè, Charles Heitz, Evangelia Christodoulou, Annika Reinke, Carole H. Sudre
Some Hidden Traps of Confidence Intervals in Medical Image Segmentation: Coverage Issues. 15-24 - Mischa Dombrowski, Bernhard Kainz
Enabling PSO-Secure Synthetic Data Sharing Using Diversity-Aware Diffusion Models. 25-35 - Kanjar De, Alireza Salehi M.:
Three Countries, Three Continents: AI Medical Device Regulation and Certification Comparison in the US, EU, and South Korea. 36-45 - Beatriz Garcia Santa Cruz
From Data to Value: Gaps in Federated Learning Evaluation for Clinical Deployment in Medical Imaging. 46-55 - Niklas Babendererde, Amin Ranem
FDA's PCCP: Opportunities and Gaps. 56-64
Proceedings of the Sixth MICCAI Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2025)
- Kaouther Mouheb, Marawan Elbatel, Janne M. Papma, Geert Jan Biessels, Jurgen A. H. R. Claassen, Huub A. M. Middelkoop, Barbara van Munster, Wiesje M. van der Flier, Inez H. G. B. Ramakers, Stefan Klein
Federated Fine-Tuning of SAM-Med3D for MRI-Based Dementia Classification. 69-79 - Elie Thellier, Huiyu Li, Nicholas Ayache, Hervé Delingette
Mitigating Data Exfiltration Attacks Through Layer-Wise Learning Rate Decay Fine-Tuning. 80-90 - Shuang Gu, Zhenyu Tang, Song Qiu, Xiaosong Wang:
Dynamic Hyperparameter Adjustment via Reinforcement Learning in Asynchronous Federated Learning for Medical Image Analysis. 91-101 - Simone Bendazzoli
MONet-FL: Extending nnU-Net with MONAI for Clinical Federated Learning. 102-110 - Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot:
FedGIN: Federated Learning with Dynamic Global Intensity Non-linear Augmentation for Organ Segmentation Using Multi-modal Images. 111-120 - Jan Fiszer, Dominika Ciupek, Maciej Malawski
Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity? 121-130 - Marc Molina Van De Bosch, Andrea Protani, Riccardo Taiello, Lorenzo Giusti, Matilde Carvalho Costa, Ioannis Stathopoulos, Efstathios P. Efstathopoulos, Diogo Reis Santos, Miguel Ángel González Ballester, Luigi Serio:
The Interplay Between Explainability and Differential Privacy in Federated Healthcare. 131-142 - Afsaneh Mahanipour, Abdullah-Al-Zubaer Imran
Federated Reprogramming Knowledge Distillation for Medical Image Classification. 143-152 - Ayomide Akinsanya, Tegan Brennan:
FedSlowdown: Efficiency Attacks Against Federated Learning of Adaptive Neural Networks. 153-163
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