Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care: First Deep Breast Workshop, Deep-Breath 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings
This book constitutes the refereed proceedings of the First Deep Breast Workshop on Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Interv...
Gespeichert in:
| Weitere Verfasser: | , , , , , , , , , , , , |
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| Dokumenttyp: | Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2025
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| Schriftenreihe: | Lecture Notes in Computer Science
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| Online-Zugang: | Verlag, Cover: https://www.dietmardreier.de/annot/564C42696D677C7C393738333033313737373838327C7C434F50.jpg?sq=3
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| Verfasserangaben: | Ritse M. Mann, Tianyu Zhang, Tao Tan, Luyi Han, Danial Truhn, Shuo Li, Yuan Gao, Shannon Doyle, Robert Martí Marly, Jakob Nikolas Kather, Katja Pinker-Domenig, Shandong Wu, Geert Litjens |
Inhaltsangabe:
- Evaluation of Bagging Ensembles on Multimodal Data for Breast Cancer Diagnosis.- HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging.- DuEU-Net: Dual Encoder UNet with Modality-Agnostic Training for PET-CT Multi-Modal Organ and Lesion Segmentation.- One for All: UNET Training on Single-Sequence Masks for Multi-Sequence Breast MRI Segmentation.- Multimodal Breast MRI Language-Image Pretraining (MLIP): An Exploration of a Breast MRI Foundation Model.- Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data.- Efficient Generation of Synthetic Breast CT Slices By Combining Generative and Super-Resolution Models.- Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification.- Virtual dynamic contrast enhanced breast MRI using 2D U-Net.- Optimizing BI-RADS 4 Lesion Assessment using Lightweight Convolutional Neural Network with CBAM in Contrast Enhanced Mammography.- Mammographic Breast Positioning Assessment via Deep Learning.- Endpoint Detection in Breast Images for Automatic Classification of Breast Cancer Aesthetic Results.- Thick Slices for Optimal Digital Breast Tomosynthesis Classification with Deep-Learning.- Predicting Aesthetic Outcomes in Breast Cancer Surgery: a Multimodal Retrieval Approach.- Vision Mamba for Classification of Breast Ultrasound Images.- Breast Cancer Molecular Subtyping from H&E Whole Slide Images using Foundation Models and Transformers.- Graph Neural Networks for modelling breast biomechanical compression.- A generative adversarial approach to remove Moiré artifacts in Dark-field and Phase-contrast x-ray images.- MRI Breast tissue segmentation using nnUNet for Biomechanical modeling.- Fat-Suppressed Breast MRI Synthesis for Domain Adaptation in Tumour Segmentation.- Guiding Breast Conservative Surgery by Augmented Reality from Preoperative MRI: Initial System Design and Retrospective Trials.- ELK: Enhanced Learning through cross-modal Knowledge transfer for lesion detection in limited-sample contrast-enhanced mammography datasets.- Safe Breast Cancer Diagnosis Resilient to Mammographic Adversarial Samples.