Less-Supervised Segmentation with CNNs : Scenarios, Models and Optimization (The Miccai Society book Series)

Dolz, Jose (EDT)/ Ben Ayed, Ismail (EDT)/ Desrosiers, Christian (EDT)

Academic Press Inc（2025/08発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 275 p.
• 言語 ENG
• 商品コード 9780323956741
• DDC分類 006.37

Full Description

Less-Supervised Segmentation with CNNs: Scenarios, Models and Optimization reviews recent progress in deep learning for image segmentation under scenarios with limited supervision, with a focus on medical imaging. The book presents main approaches and state-of-the-art models and includes a broad array of applications in medical image segmentation, including healthcare, oncology, cardiology and neuroimaging. A key objective is to make this mathematical subject accessible to a broad engineering and computing audience by using a large number of intuitive graphical illustrations. The emphasis is on giving conceptual understanding of the methods to foster easier learning.

This book is highly suitable for researchers and graduate students in computer vision, machine learning and medical imaging.

Contents

1. Introduction
2. Preliminaries
3. Different levels of supervision
4. Semi-supervised learning
5. Unsupervised domain adaptation
6. Weakly supervised segmentation
7. Few-shot learning
8. Unsupervised segmentation
9. Perspectives and future directions