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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 VII

  • 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 15966)

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-lii
    Download chapter PDF

  2. Trustworthy and Responsible AI for Medical Imaging

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. A Multimodal Contrastive Learning for Detecting Aortic Dissection on 3D Non-contrast CT with Anatomy Simplification

      • Duoer Zhang, Wenbo Xiao, Chen Jiang, Yuxuan Qiu, Zhan Feng, Hong Wang et al.
      Pages 3-12

    3. Adaptive Adversarial Data Augmentation with Trajectory Constraint for Alzheimer’s Disease Conversion Prediction

      • Hyuna Cho, Hayoung Ahn, Guorong Wu, Won Hwa Kim
      Pages 13-23

    4. Adaptive Stain Normalization for Cross-Domain Medical Histology

      • Tianyue Xu, Yanlin Wu, Abhai K. Tripathi, Matthew M. Ippolito, Benjamin D. Haeffele
      Pages 24-33

    5. Addressing Label Scarcity and Domain Shift in Medical Image Segmentation

      • Suruchi Kumari, Pravendra Singh
      Pages 34-44

    6. AEM: Attention Entropy Maximization for Multiple Instance Learning Based Whole Slide Image Classification

      • Yunlong Zhang, Honglin Li, Yuxuan Sun, Zhongyi Shui, Jingxiong Li, Chenglu Zhu et al.
      Pages 45-55

    7. Automated Detection of Abnormalities in Zebrafish Development

      • Sarath Sivaprasad, Hui-Po Wang, Anna-Lisa Jäckel, Jonas Baumann, Carole Baumann, Jennifer Herrmann et al.
      Pages 56-66

    8. Automatic Dataset Shift Identification to Support Safe Deployment of Medical Imaging AI

      • Mélanie Roschewitz, Raghav Mehta, Charles Jones, Ben Glocker
      Pages 67-76

    9. BaMCo: Balanced Multimodal Contrastive Learning for Knowledge-Driven Medical VQA

      • Ziya Ata Yazici, Hazim Kemal Ekenel
      Pages 77-87

    10. BiasICL: In-Context Learning and Demographic Biases of Vision Language Models

      • Sonnet Xu, Joseph D. Janizek, Yixing Jiang, Roxana Daneshjou
      Pages 88-97

    11. BrainAlign: EEG-Vision Alignment via Frequency-Aware Temporal Encoder and Differentiable Cluster Assigner

      • Enze Shi, Huawen Hu, Qilong Yuan, Kui Zhao, Sigang Yu, Shu Zhang
      Pages 98-108

    12. Contour Makes it Stronger: Cross-Domain Cephalometric Landmark Detection Based on Contour Priors

      • Xinyue Liang, Runnan Chen, Guangshun Wei, Shaojie Zhuang, Yuanfeng Zhou
      Pages 109-118

    13. CXR-CML: Improved Zero-Shot Classification of Long-Tailed Multi-label Diseases in Chest X-Rays

      • Rajesh Madhipati, Sheethal Bhat, Lukas Buess, Andreas Maier
      Pages 119-129

    14. Deep Learning Framework for Managing Inter-reader Variability in Background Parenchymal Enhancement Classification for Contrast-Enhanced Mammography

      • Elodie Ripaud, Clément Jailin, Pablo Milioni de Carvalho, Laurence Vancamberg, Isabelle Bloch
      Pages 130-140

    15. Dia-LLaMA: Towards Large Language Model-Driven CT Report Generation

      • Zhixuan Chen, Luyang Luo, Yequan Bie, Hao Chen
      Pages 141-151

    16. Diff-RRG: Longitudinal Disease-Wise Patch Difference as Guidance for LLM-Based Radiology Report Generation

      • Hannah Yun, Junyeong Maeng, Eunsong Kang, Heung-Il Suk
      Pages 152-161

    17. Distilling Foundation Models for Robust and Efficient Models in Digital Pathology

      • Alexandre Filiot, Nicolas Dop, Oussama Tchita, Auriane Riou, Rémy Dubois, Thomas Peeters et al.
      Pages 162-172

    18. Domain Generalization for Mammogram Classification by Suppressing Domain-Specific Features

      • Jiqun Chen, Luhao Sun, Wenzong Jiang, Weifeng Liu, Chao Li, Zhiyong Yu et al.
      Pages 173-183

    19. Domain-Adaptive Diagnosis of Lewy Body Disease with Transferability Aware Transformer

      • Xiaowei Yu, Jing Zhang, Tong Chen, Yan Zhuang, Minheng Chen, Chao Cao et al.
      Pages 184-193
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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 VII

  • 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/10.1007/978-3-032-04981-0

  • 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: 978-3-032-04980-3Published: 20 September 2025

  • eBook ISBN: 978-3-032-04981-0Published: 19 September 2025

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: LII, 705

  • Number of Illustrations: 11 b/w illustrations, 195 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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