Less-supervised segmentation with CNNs : scenarios, models and optimization /

Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Dolz, Jose, (Of Université du Québec, École de technologie supérieure), Ben Ayed, Ismail (Editor), Desrosiers, Christian, 1977- (Editor)
Format:	eBook
Language:	English
Published:	London, U.K. ; Cambridge, MA : Academic Press, [2026]
Series:	Elsevier and MICCAI Society book series.
Subjects:	Image segmentation. Diagnostic imaging > Data processing. Neural networks (Computer science) Computer vision. Deep learning (Machine learning) Segmentation d'image. Réseaux neuronaux (Informatique) Vision par ordinateur. Apprentissage profond. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

General introduction
A Unified View of Learning from Both Labeled and Unlabeled Data
Semi-Supervised Learning
Weakly Supervised Segmentation
Alternative Learning Scenarios
Contrastive Learning for Unsupervised Representation and Semi-Supervised Learning for Medical Image Segmentation
Boosting Semi-Supervised Image Segmentation with Global and Local Mutual Information Regularization
Deep Model Adaptation Without Target Labels on Cross-Domain Medical Images
Advancing Medical Image Segmentation via Exploiting Limited Annotations
Blending Variational Approaches and Deep Learning to Enforce Prior Constraints in Medical Image Segmentation
Self- and Unsupervised Learning for Anomaly Detection and Localization
Learning from Limited Representation
Transductive Few-Shot Adapters for Medical Image Segmentation
AI methodologies for multimodal pathology applications.