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

• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
• Edizione: 1st ed. 2025.
• Descrizione fisica: 1 online resource (251 pages)
• Disciplina: 616.07540285
• Soggetto topico: Image processing - Digital techniques; Computer vision; Machine learning; Computers; Application software; Computer Imaging, Vision, Pattern Recognition and Graphics; Machine Learning; Computing Milieux; Computer and Information Systems Applications
• Persona (resp. second.): AlīŚāriba
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Cancer Prevention, Detection, and Intervention
• ISBN: 3-031-73376-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910983342203321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 15199