Daniel C. Alexander

Polina Golland

Archana Venkataraman

Carole H. Sudre

James C. Gee

Jaesung Hong

Juan Eugenio Iglesias

Jong Hyo Kim

Jinah Park

The 16-volume set LNCS 15960 - 15975 constitutes the refereed proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, which took place in Daejeon, South Korea, during September 23–27, 2025. The total of 1027 papers included in the proceedings was carefully reviewed and selected from 3447 submissions. They were organized in topical parts as follows: Part I, LNCS Volume 15960: Multimodal Fusion and Contextual Reasoning in Medical Imaging Part II, LNCS Volume 15961: Surgical Navigation, Scene Understanding, and Video Modeling Part III, LNCS Volume 15962: Learning and Augmented Reality for Surgical and Endoscopic Applications (I) Part IV, LNCS Volume 15963: Learning and Augmented Reality for Surgical and Endoscopic Applications (II) Part V, LNCS Volume 15964: Graph-Based Methods in Medical Imaging Part VI, LNCS Volume 15965: Datasets and Methods for Image Quality Enhancement Part VII, LNCS Volume 15966: Trustworthy and Responsible AI for Medical Imaging Part VIII, LNCS Volume 15967: Multimodal Learning for Diagnosis, Risk Prediction, and Survival Analysis Part IX, LNCS Volume 15968: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (I) Part X, LNCS Volume 15969: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (II) Part XI, LNCS Volume 15970: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (III) Part XII, LNCS Volume 15971: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (IV) Part XIII, LNCS Volume 15972: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (I) Part XIV, LNCS Volume 15973: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (II) Part XV, LNCS Volume 15974: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (III) Part XVI, LNCS Volume 15975: Statistical Techniques in Medical Imaging: Causality, Imputation, Weak Supervision, and Other Methods

28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part XII

Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

Christian F. Baumgartner

Adrian Dalca

Ryutaro Tanno

Koen Van Leemput

William M. Wells III

Chen Qin

This book constitutes the refereed proceedings of the Fourth Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with MICCAI 2022. The conference was hybrid event held from Singapore. For this workshop, 13 papers from 22 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world.

4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

William M. Wells

Hamid Fehri

Tal Arbel

Aristeidis Sotiras

Bartlomiej Papiez

Enzo Ferrante

Sarah Parisot

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

Carole H. Sudre

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