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Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

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

  • Conference proceedings
  • © 2026

Overview

Editors:
  1. James C. Gee

    1. University of Pennsylvania, Philadelphia, USA

  2. Daniel C. Alexander

    1. University College London, London, UK

  3. Jaesung Hong

    1. DGIST, Daegu, Korea (Republic of)

  4. Juan Eugenio Iglesias

    1. Massachusetts General Hospital and Harvard Medical School, Charlestown, USA

  5. Carole H. Sudre

    1. University College London, London, UK

  6. Archana Venkataraman

    1. Boston University, Boston, USA

  7. Polina Golland

    1. MIT, Cambridge, USA

  8. Jong Hyo Kim

    1. Seoul National University, Seoul, Korea (Republic of)

  9. Jinah Park

    1. KAIST, Daejeon, Korea (Republic of)

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15973)

Included in the following conference series:

Conference proceedings info: MICCAI 2025.

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About this book

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

 

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Table of contents (65 papers)

  1. Front Matter

    Pages i-li
    Download chapter PDF

  2. Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (II)

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Adaptive Frame Selection for Gestational Age Estimation from Blind Sweep Fetal Ultrasound Videos

      • Tanya Akumu, Marawan Elbatel, Victor M. Campello, Richard Osuala, Carlos Martin-Isla, Ignacio Valenzuela et al.
      Pages 3-12

    3. An Anatomical Significance-Aware Architecture for Explainable Myocardial Infarction Prediction via Multi-task Learning

      • Jiachuan Peng, Marcel Beetz, Abhirup Banerjee, Min Chen, Vicente Grau
      Pages 13-23

    4. Bias and Generalizability of Foundation Models Across Datasets in Breast Mammography

      • Elodie Germani, Ilayda Selin-Türk, Fatima Zeineddine, Charbel Mourad, Shadi Albarqouni
      Pages 24-34

    5. Bridging Knowledge Discrepancy in Retinal Image Analysis Through Federated Multi-task Learning

      • Jing Yang, Yuxi Ma, Jin-Gang Yu, Feng Gao, Shuting Yang, Du Cai et al.
      Pages 35-44

    6. Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography

      • Jacopo Teneggi, J. Webster Stayman, Jeremias Sulam
      Pages 45-55

    7. Conservative-Radical Complementary Learning for Class-Incremental Medical Image Analysis with Pre-trained Foundation Models

      • Xinyao Wu, Zhe Xu, Donghuan Lu, Jinghan Sun, Hong Liu, Sadia Shakil et al.
      Pages 56-66

    8. CoPA: Hierarchical Concept Prompting and Aggregating Network for Explainable Diagnosis

      • Yiheng Dong, Yi Lin, Xin Yang
      Pages 67-76

    9. CytoSAE: Interpretable Cell Embeddings for Hematology

      • Muhammed Furkan Dasdelen, Hyesu Lim, Michele Buck, Katharina S. Götze, Carsten Marr, Steffen Schneider
      Pages 77-86

    10. DCKAN: A Dual-Coordinate KAN Framework for Fibrous Cap Segmentation on Carotid OCT

      • Tonghua Wan, Sihan Liu, Yuxin Cai, Shengcai Chen, Yan Wan, Bo Hu et al.
      Pages 87-96

    11. Decentralized Noise Handling in Medical Imaging: Encoder-Decoder Based Federated Imputation for Robust Training

      • Yunyoung Chang, Yeonwoo Noh, Sang-Woong Lee, Minwoo Lee, Wonjong Noh
      Pages 97-105

    12. DeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology

      • Yousef Yeganeh, Maximilian Frantzen, Michael Lee, Kun-Hsing Yu, Nassir Navab, Azade Farshad
      Pages 106-116

    13. Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction

      • Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur, Sara P. Oliveira, Sanne Abeln, Wilson Silva
      Pages 117-127

    14. E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation

      • Bin Huang, Zhong Liu, Huiying Wen, Bingsheng Huang, Xin Chen, Shuo Li
      Pages 128-137

    15. Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEs

      • Francesco Di Salvo, Hanh Huyen My Nguyen, Christian Ledig
      Pages 138-147

    16. Enhancing AI-Assisted Stroke Emergency Triage with Adaptive Uncertainty Estimation

      • Shuhua Yang, Tongan Cai, Haomiao Ni, Wenchao Ma, Yuan Xue, Kelvin Wong et al.
      Pages 148-157

    17. Enhancing Soft Tissue Sarcoma Classification by Mitigating Patient-Specific Bias in Whole Slide Images

      • Weiping Lin, Runchen Zhu, Wentai Hou, Jiacheng Wang, Yixuan Lin, Rui Chen et al.
      Pages 158-167

    18. Equitable Federated Learning with NCA

      • Nick Lemke, Mirko Konstantin, Henry John Krumb, John Kalkhof, Jonathan Stieber, Anirban Mukhopadhyay
      Pages 168-177

    19. FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation

      • Haolong Jin, Shenglin Liu, Cong Cong, Qingmin Feng, Yongzhi Liu, Lina Huang et al.
      Pages 178-188
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  7. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  8. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  9. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  10. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  11. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  12. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  13. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  14. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  15. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  16. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  17. Bridging Regulatory Science and Medical Imaging Evaluation; and Distributed, Collaborative, and Federated Learning

Editors and Affiliations

  • University of Pennsylvania, Philadelphia, USA

    James C. Gee

  • University College London, London, UK

    Daniel C. Alexander, Carole H. Sudre

  • DGIST, Daegu, Korea (Republic of)

    Jaesung Hong

  • Massachusetts General Hospital and Harvard Medical School, Charlestown, USA

    Juan Eugenio Iglesias

  • Boston University, Boston, USA

    Archana Venkataraman

  • MIT, Cambridge, USA

    Polina Golland

  • Seoul National University, Seoul, Korea (Republic of)

    Jong Hyo Kim

  • KAIST, Daejeon, Korea (Republic of)

    Jinah Park

Accessibility Information

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This ebook is designed with accessibility in mind, aiming to meet the ePub Accessibility 1.0 AA and WCAG 2.2 Level AA standards. It features a navigable table of contents, structured headings, and alternative text for images, ensuring smooth, intuitive navigation and comprehension. The text is reflowable and resizable, with sufficient contrast. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at [email protected].

Bibliographic Information

  • Book Title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

  • Book Subtitle: 28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part XIV

  • Editors: James C. Gee, Daniel C. Alexander, Jaesung Hong, Juan Eugenio Iglesias, Carole H. Sudre, Archana Venkataraman, Polina Golland, Jong Hyo Kim, … Jinah Park

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-032-05185-1

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026

  • Softcover ISBN: 978-3-032-05184-4Published: 21 September 2025

  • eBook ISBN: 978-3-032-05185-1Published: 19 September 2025

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: LI, 695

  • Number of Illustrations: 3 b/w illustrations, 200 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Computer Applications, Machine Learning, Computers and Education, Biomedical Engineering and Bioengineering

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