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El. knyga: 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

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  • Formatas: EPUB+DRM
  • Serija: Lecture Notes in Computer Science 15451
  • Išleidimo metai: 11-Feb-2025
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783031777899
Kitos knygos pagal šią temą:
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, ProceedingsDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Serija: Lecture Notes in Computer Science 15451
  • Išleidimo metai: 11-Feb-2025
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783031777899
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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 Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 10, 2024.The 23 regular papers presented in this book were carefully reviewed and selected from 51 submissions.The workshop provides an international platform for presentation of - and discussion on - studies related to AI in breast imaging. Deep-Breath aims to promote the development of this research area by sharing insights in academic research and clinical practice between clinicians and AI experts, and by exploring together the opportunities and potential challenges of AI applications in breast health. The deep-breath workshop provides, therefore, an unique forum to discuss the possibilities in this challenging field, aiming to create value that eventually truly leads to benefit for physicians and patients.
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.
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