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  • Produktbild: Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care
  • Produktbild: Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care
Band 15451

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

Aus der Reihe Lecture Notes in Computer Science

Fr. 85.90

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.02.2025

Abbildungen

XI, 246 p. 78 illus., 68 illus. in color.

Herausgeber

Tianyu Zhang + weitere

Verlag

Springer

Seitenzahl

246

Maße (L/B/H)

23.5/15.5/1.5 cm

Gewicht

400 g

Sprache

Englisch

ISBN

978-3-031-77788-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.02.2025

Abbildungen

XI, 246 p. 78 illus., 68 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

246

Maße (L/B/H)

23.5/15.5/1.5 cm

Gewicht

400 g

Sprache

Englisch

ISBN

978-3-031-77788-2

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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

  • 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.