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ORCID2nd Deep-Breath@MICCAI 2025: Daejeon, South Korea
- Tianyu Zhang
, Oliver Lester Saldanha, Luyi Han, Nika Rasoolzadeh, Lidia Garrucho Moras, Jarek van Dijk, Tao Tan, Jakob Nikolas Kather, Ritse Mann:
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. Lecture Notes in Computer Science 16142, Springer 2026, ISBN 978-3-032-05558-3 - Hongxu Yang, Edina Timko, Levente Lippenszky, Vanda Czipczer, Lehel Ferenczi:
SynBT: High-Quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model. 1-10 - Krishna Kanth Nakka:
Mammo-SAE : Interpreting Breast Cancer Concept Learning with Sparse Autoencoders. 11-20 - Rebecca Mes, Mark Wijkhuizen, Lennard M. van Karnenbeek, Tao Tan, Ritse Mann, Theo Ruers, Freija Geldof, Behdad Dashtbozorg:
Training Set Diversity: A Key Factor in AI-Driven Breast Ultrasound Classification. 21-30 - Faisal Ahmed, David Sánchez, Josep Domingo-Ferrer, Zouhair Haddi:
FedBC: Privacy-Preserving Breast Cancer Diagnosis from Ultrasound Images Using Federated Learning. 31-40 - Mohammad Hossein Zolfagharnasab, Tiago Gonçalves, Pedro Ferreira, Maria João Cardoso, Jaime S. Cardoso:
Towards Robust Breast Segmentation: Leveraging Depth Awareness and Convexity Optimization For Tackling Data Scarcity. 41-51 - Eleonora Poeta, Luisa Vargas, Daniele Falcetta, Vincenzo Marcianó, Eliana Pastor, Tania Cerquitelli, Elena Baralis, Maria A. Zuluaga:
Divergence-Aware Training with Automatic Subgroup Mitigation for Breast Tumor Segmentation. 52-62 - Manon A. Dorster, Felix J. Dorfner, Mason C. Cleveland, Melisa S. Guelen, Jay B. Patel, Dania Daye, Jean-Philippe Thiran, Albert E. Kim, Christopher P. Bridge:
Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging. 63-71 - Toufiq Musah:
Large Kernel MedNeXt for Breast Tumor Segmentation and Self-normalizing Network for pCR Classification in Magnetic Resonance Images. 72-80 - Solha Kang, Eugene Kim, Joris Vankerschaver, Utku Ozbulak:
Towards Affordable Tumor Segmentation and Visualization for 3D Breast MRI Using SAM2. 81-90 - Han Chen, Anne L. Martel:
Breast Cancer Detection from Multi-view Screening Mammograms with Visual Prompt Tuning. 91-100 - Hanxue Gu, Yaqian Chen, Nicholas Konz, Qihang Li, Maciej A. Mazurowski:
Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration? 101-112 - Adnan Khalid, Muhammad Mursil, Fabrice Mériaudeau, Ibrahima Faye, Alain Lalande, Domenec Puig, Hatem A. Rashwan:
Towards Breast Cancer Recurrence Prediction Using Transformer-Based Learning from Global-Local Radiomics and Clinical Data. 113-122 - Jakub Ceranka, Diego Lamtenzan, Pieter Thomas Boonen, Ayush Kapila, Carola Brussaard, Moustapha Hamdi, Jef Vandemeulebroucke:
Computer-Assisted Surgical Planning for DIEP Flap Breast Reconstruction Surgery. 123-133 - Tewele W. Tareke, Neree Payan, Alexandre Cochet, Alain Lalande, Fabrice Mériaudeau:
MM + CD Fusion: Deep Learning-based 3D Multi-Modal Fusion for Early Pathological Complete Response Prediction in Breast Cancer. 134-144 - Carlos Santiago, Jacinto C. Nascimento:
Exploring Synergies Between Convolutional Neural Networks and Transformers for Breast Cancer Segmentation. 145-154 - Daan Schouten, Jeong Hoon Lee, Gregory R. Bean, Haruka Itakura, Mirabela Rusu:
A Domain-Inspired, Semi-supervised nnU-Net Is All You Need for Primary Breast Cancer Segmentation. 155-164 - Lauren Jimenez-Martin, Felip Vilardell, Ana Petit, Eduard Dorca, Teresa Soler-Monsó, Irma Ramos-Oliver, Laura Pons, Pedro L. Fernández, Laia Adalid Llansa, Daniel Mata, Lourdes Salazar-Huayna, Josep R. Casas, Verónica Vilaplana:
Segmenting Invasive and In Situ Carcinoma in Breast WSIs with a Pretrained Histopathology Transformer. 165-175 - Luís F. Teixeira, Helena Montenegro, Eduard Bonci, Maria João Cardoso, Jaime S. Cardoso:
SiameseOrdinalCLIP: A Language-Guided Siamese Network for the Aesthetic Evaluation of Breast Cancer Locoregional Treatment. 176-185 - Joana Santos, Helena Montenegro, Eduard Bonci, Maria João Cardoso, Jaime S. Cardoso:
Anatomically and Clinically Informed Deep Generative Model for Breast Surgery Outcome Prediction. 186-195 - Qihang Li, Jichen Yang, Yaqian Chen, Yuwen Chen, Hanxue Gu, Lars J. Grimm, Maciej A. Mazurowski:
BreastSegNet: Multi-label Segmentation of Breast MRI. 196-205 - Bulat Maksudov, Kathleen M. Curran, Alessandra Mileo:
Anatomy-Preserving Counterfactual Edits in Breast MRI via Guided Diffusion. 206-215 - Aiman Farooq, Chandisha Das, Deepak Mishra:
Uncertainty-Aware Cross-Modal Attention for Breast Cancer Classification in Ultrasound Imaging. 216-225 - Sebastian Ibarra, Javier del Riego, Alessandro Catanese, Julian Cuba, Julian Cardona, Nataly Leon, Jonathan Infante, Karim Lekadir, Oliver Díaz, Richard Osuala:
Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-contrast Images. 226-236 - André C. Castro, Dayane Rodrigues, Arlindo R. G. Filho:
Adapting Vision Language Models for Structured Clinical Description Generation in Mammography. 237-247 - Hadeel Awwad, Joan Carles Vilanova, Robert Martí:
Can We Teach AI to Understand Breast Tumour Behaviour? Our MAMA-MIA Challenge Journey. 248-257 - Karim Elbarbary, Adarsh Bhandary Panambur, Sheethal Bhat, Siming Bayer, Andreas Maier:
MM-DETR: Emulating the Diagnostic Clinical Workflow in Multi-view Multi-modal Mammography Mass Detection. 258-267 - Pedro Ferreira, Mohammad Hossein Zolfagharnasab, Tiago Gonçalves, Eduard Bonci, Carlos Mavioso, Maria João Cardoso, Jaime S. Cardoso:
Predicting Aesthetic Outcomes of Breast Cancer Surgery: A Robust and Explainable Image Retrieval Approach. 268-278 - Gonçalo Pinto, Mohammad Hossein Zolfagharnasab, Luís F. Teixeira, Helena Cruz, Maria João Cardoso, Jaime S. Cardoso:
Towards Utilizing Robust Radiance Fields for 3D Reconstruction of Breast Aesthetics. 279-288 - Adaobi Emegoakor, Raymond Confidence, Yewande Gbadamosi, Richard Malumba, Charity Umoren, Diana Spence Betancourt, Aondona M. Iorumbur, Chinasa Kalaiwo, Abbas Rabiu Muhammad, Dennis Musinguzi, Patience Atukunda, Peter Makhoul, Rosta Asiimwe, Alfred Jatho, Amaka Nnamani, Franca Eze, Oluyemisi Toyobo, Abiodun Fatade, Udunna C. Anazodo, Farouk Dako, Michael Kawooya, Maruf Adewole:
Bridging the Gap: A Community Driven and AI-Enabled Approach to Early Breast Cancer Detection in Black African Women. 289-299 - Lea Schwarz, Ricardo Montoya-del-Angel, Marawan Elbatel, Robert Marti:
Mammographic Image Generation Using Generative Cellular Automata. 300-309 - Jessica Kächele, Dimitrios Bounias, Alexandra Ertl, Klaus H. Maier-Hein:
On Tackling Domain Shift in Breast MRI Using Only Publicly-Available Data: Reproducible Breast Cancer Segmentation and pCR Prediction. 310-319 - Alba Bernal Rodriguez, Beatriz Remeserio, Justin Engelmann, Lucas Gago, Jacinto Velasco Rebolledo:
Input Simplification Impact on Robustness for Targeted Therapy Subtypes in Breast MRI Segmentation AI. 320-328 - Mohammed Kamran, Maria Bernathova, Raoul Varga, Christian F. Singer, Zsuzsanna Bago-Horvath, Thomas H. Helbich, Georg Langs, Philipp Seeböck:
LesiOnTime - Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI. 329-339 - Tajamul Ashraf, Suhaib Salmani, Mohammed Mohsen Peerzada, Ufaq Khan, Yutong Xie, Janibul Bashir:
MedMask: A Self-supervised Vision Foundation Model for Breast Cancer Detection Using Mammograms. 340-350 - Zhengbo Zhou, Degan Hao, Dooman Arefan, Margarita L. Zuley, Jules H. Sumkin, Lars Grimm, James Joshi, Shandong Wu:
Longitudinal Mammogram Exam-Based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attacks. 351-361
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