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Author:   Sharib Ali ,  Fons van der Sommen ,  Bartłomiej Władysław Papież ,  Noha Ghatwary
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2025 ed.
Volume:   15199
ISBN:  

9783031733758

ISBN 10:   3031733754
Pages:   243
Publication Date:   09 October 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Classification and characterization.- Multi-center ovarian tumor classification using hierarchical transformer-based multiple-instance learning.- FoTNet Enables Preoperative Differentiation of Malignant Brain Tumors with Deep Learning.- Classification of Endoscopy and Video Capsule Images using Hybrid Model.- Multimodal Deep Learning-based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases.- Seeing More with Less: Meta-Learning and Diffusion Models for Tumor Characterization in Low-data Settings.- Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification.- Detection and Segmentation.- On undesired emergent behaviors in compound prostate cancer detection systems.- Optimizing Multi-Expert Consensus for Classification and Precise Localization of Barrett’s Neoplasia.- Automated Hepatocellular Carcinoma Analysis in Multi-Phase CT with Deep Learning.- Refining deep learning segmentation maps with a local thresholding approach: application to liver surface nodularity quantification in CT.- Uncertainty-Aware Deep Learning Classification for MRI-based Prostate Cancer Detection.- Generalized Polyp Detection from Colonoscopy frames Using proposed EDF-YOLO8 Network.- AI-Assisted Laryngeal Examination System.- UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak Supervision.- SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling.- Cancer/Early cancer detection, treatment, and survival prognosis.-AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients.- Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery.- Treatment efficacy prediction of focused ultrasound therapies using multi-parametric magnetic resonance imaging.- SurRecNet: A Multi-Task Model with Integrating MRI and Diagnostic Descriptions for Rectal Cancer Survival Analysis.- Improved prediction of recurrence after prostate cancer radiotherapy using multimodal data and in silico simulations.- AutoDoseRank: Automated Dosimetry-informed Segmentation Ranking for Radiotherapy.- SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network.

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