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Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care

Second Deep Breast Workshop, Deep-Breath 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings

  • Conference proceedings
  • © 2026

Overview

Editors:
  1. Tianyu Zhang

    1. Radboudumc, Nijmegen, The Netherlands

  2. Oliver Lester Saldanha

    1. Dresden University of Technology, Dresden, Germany

  3. Luyi Han

    1. Radboudumc, Nijmegen, The Netherlands

  4. Nika Rasoolzadeh

    1. Radboudumc, Nijmegen, The Netherlands

  5. Lidia Garrucho Moras

    1. University of Barcelona, Barcelona, Spain

  6. Jarek van Dijk

    1. Radboudumc, Nijmegen, The Netherlands

  7. Tao Tan

    1. Macao Polytechnic University, Macao, China

  8. Jakob Nikolas Kather

    1. Dresden University of Technology, Dresden, Germany

  9. Ritse Mann

    1. Radboudumc, Nijmegen, The Netherlands

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 16142)

Included in the following conference series:

Conference proceedings info: Deep-Breath 2025.

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About this book

This book constitutes the refereed proceedings of the Second Deep Breast Workshop on Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2025, held in conjunction with the 27th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.

The 35 regular papers presented in this book were carefully reviewed and selected from 62 submissions.The Deep-Breath workshop provides an international platform for the presentation of – and discussion on – studies related to artificial intelligence in breast imaging. By fostering collaboration between clinicians and AI experts,
the workshop promotes the development and responsible application of AI technologies across the breast cancer care continuum. Deep-Breath aims to create a unique forum where technical advances and clinical insights converge, driving innovation that ultimately leads to real-world impact for both physicians and patients.

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Table of contents (35 papers)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. SynBT: High-Quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model

    • Hongxu Yang, Edina Timko, Levente Lippenszky, Vanda Czipczer, Lehel Ferenczi
    Pages 1-10

  3. Mammo-SAE : Interpreting Breast Cancer Concept Learning with Sparse Autoencoders

    • Krishna Kanth Nakka
    Pages 11-20

  4. Training Set Diversity: A Key Factor in AI-Driven Breast Ultrasound Classification

    • Rebecca Mes, Mark Wijkhuizen, Lennard M. van Karnenbeek, Tao Tan, Ritse Mann, Theo Ruers et al.
    Pages 21-30

  5. FedBC: Privacy-Preserving Breast Cancer Diagnosis from Ultrasound Images Using Federated Learning

    • Faisal Ahmed, David Sánchez, Josep Domingo-Ferrer, Zouhair Haddi
    Pages 31-40

  6. Towards Robust Breast Segmentation: Leveraging Depth Awareness and Convexity Optimization For Tackling Data Scarcity

    • Mohammad Hossein Zolfagharnasab, Tiago Gonalves, Pedro Ferreira, Maria J. Cardoso, Jaime S. Cardoso
    Pages 41-51

  7. Divergence-Aware Training with Automatic Subgroup Mitigation for Breast Tumor Segmentation

    • Eleonora Poeta, Luisa Vargas, Daniele Falcetta, Vincenzo Marciano’, Eliana Pastor, Tania Cerquitelli et al.
    Pages 52-62

  8. Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging

    • Manon A. Dorster, Felix J. Dorfner, Mason C. Cleveland, Melisa S. Guelen, Jay Patel, Dania Daye et al.
    Pages 63-71

  9. Large Kernel MedNeXt for Breast Tumor Segmentation and Self-normalizing Network for pCR Classification in Magnetic Resonance Images

    • Toufiq Musah
    Pages 72-80

  10. Towards Affordable Tumor Segmentation and Visualization for 3D Breast MRI Using SAM2

    • Solha Kang, Eugene Kim, Joris Vankerschaver, Utku Ozbulak
    Pages 81-90

  11. Breast Cancer Detection from Multi-view Screening Mammograms with Visual Prompt Tuning

    • Han Chen, Anne L. Martel
    Pages 91-100

  12. Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?

    • Hanxue Gu, Yaqian Chen, Nicholas Konz, Qihang Li, Maciej A. Mazurowski
    Pages 101-112

  13. Towards Breast Cancer Recurrence Prediction Using Transformer-Based Learning from Global–Local Radiomics and Clinical Data

    • Adnan Khalid, Muhammad Mursil, Fabrice Meriaudeau, Ibrahima Faye, Alain Lalande, Domenec Puig et al.
    Pages 113-122

  14. Computer-Assisted Surgical Planning for DIEP Flap Breast Reconstruction Surgery

    • Jakub Ceranka, Diego Lamtenzan, Pieter Boonen, Ayush Kapila, Carola Brussaard, Moustapha Hamdi et al.
    Pages 123-133

  15. MM + CD Fusion: Deep Learning-based 3D Multi-Modal Fusion for Early Pathological Complete Response Prediction in Breast Cancer

    • Tewele W. Tareke, Neree Payan, Alexandre Cochet, Alain Lalande, Fabrice Meriaudeau
    Pages 134-144

  16. Exploring Synergies Between Convolutional Neural Networks and Transformers for Breast Cancer Segmentation

    • Carlos Santiago, Jacinto C. Nascimento
    Pages 145-154

  17. A Domain-Inspired, Semi-supervised nnU-Net Is All You Need for Primary Breast Cancer Segmentation

    • Daan Schouten, Jeong Hoon Lee, Gregory Bean, Haruka Itakura, Mirabela Rusu
    Pages 155-164

  18. Segmenting Invasive and In Situ Carcinoma in Breast WSIs with a Pretrained Histopathology Transformer

    • Lauren Jimenez-Martin, Felip Vilardell, Ana Petit, Eduard Dorca, Teresa Soler-Monsó, Irma Ramos-Oliver et al.
    Pages 165-175

  19. SiameseOrdinalCLIP: A Language-Guided Siamese Network for the Aesthetic Evaluation of Breast Cancer Locoregional Treatment

    • Fábio Teixeira, Helena Montenegro, Eduard Bonci, Maria J. Cardoso, Jaime S. Cardoso
    Pages 176-185

  20. Anatomically and Clinically Informed Deep Generative Model for Breast Surgery Outcome Prediction

    • Joana Santos, Helena Montenegro, Eduard Bonci, Maria J. Cardoso, Jaime S. Cardoso
    Pages 186-195
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Other volumes

  1. Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care

Editors and Affiliations

  • Radboudumc, Nijmegen, The Netherlands

    Tianyu Zhang, Luyi Han, Nika Rasoolzadeh, Jarek van Dijk, Ritse Mann

  • Dresden University of Technology, Dresden, Germany

    Oliver Lester Saldanha, Jakob Nikolas Kather

  • University of Barcelona, Barcelona, Spain

    Lidia Garrucho Moras

  • Macao Polytechnic University, Macao, China

    Tao Tan

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Bibliographic Information

  • Book Title: Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care

  • Book Subtitle: Second Deep Breast Workshop, Deep-Breath 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings

  • Editors: Tianyu Zhang, Oliver Lester Saldanha, Luyi Han, Nika Rasoolzadeh, Lidia Garrucho Moras, Jarek van Dijk, Tao Tan, Jakob Nikolas Kather, … Ritse Mann

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-032-05559-0

  • 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: 978-3-032-05558-3Published: 26 September 2025

  • eBook ISBN: 978-3-032-05559-0Published: 25 September 2025

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XV, 365

  • Number of Illustrations: 8 b/w illustrations, 109 illustrations in colour

  • Topics: Artificial Intelligence

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