影响因子：7.7
DOI码：10.1016/j.compbiomed.2023.107840
发表刊物：Computers in Biology and Medicine
项目来源：国家自然科学基金
关键字：Medical Image Segmentation，Semi-Supervised Learning，Convolutional Neural Network，Survey
摘要：Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation.
论文类型：期刊论文
论文编号：MEDLINE:38157773
一级学科：生物医学工程
文献类型：期刊
卷号：169
页面范围：107840
ISSN号：1879-0534
是否译文：否
发表时间：2024-02-01
收录刊物：SCI、SSCI