Cancer Prevention, Detection, and Intervention : Third MICCAI Workshop, CaPTion 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings / edited by Sharib Ali, Fons van der Sommen, Bartłomiej Władysław Papież, Noha Ghatwary, Yueming Jin, Iris Kolenbrander.

Format:

Book

Contributor:

Alī, Śāriba, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 15199

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Machine learning.

Computers.

Application software.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Machine Learning.

Computing Milieux.

Computer and Information Systems Applications.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Machine Learning.

Computing Milieux.

Computer and Information Systems Applications.

Physical Description:

1 online resource (251 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This book constitutes the refereed proceedings of the Third International Workshop on Cancer Prevention Through Early Detection, CaPTion, held in conjunction with the 27th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024. The 22 full papers presented in this book were carefully reviewed and selected from 25 submissions. They were organized in topical sections as follows: Classification and characterization; detection and segmentation; cancer/early cancer detection, treatment and survival prognosis.

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.

Notes:

Includes bibliographical references and index.

ISBN:

3-031-73376-2