电子科技2024，Vol.37Issue(1)：17-23,7.DOI:10.16180/j.cnki.issn1007-7820.2024.01.003
基于元学习和神经架构搜索的半监督医学图像分割方法
Semi-Supervised Medical Image Segmentation Method Based on Meta-Learning and Neural Architecture Search
摘要
Abstract
Most medical image segmentation methods mainly focus on training and evaluating in the same or similar medical data domain,which need lots of pixel-level annotations.However,these models face challenges in out-of-distribution medical data set,which is known as"domain shift"problem.A fixed U-shaped segmentation structure is usually used to solve this problem,resulting in it not being better adapted to specific partition tasks.A gradient-based meta-learning and neural architecture search method is proposed in this study,which can adjust the segmentation network according to specific tasks to achieve good performance and have good generalization ability.This method mainly uses the specific task to carry out the architecture search module to further improve the segmenta-tion effect,and then uses the gradient-based meta-learning training algorithm to improve the generalization ability.On the public dataset M&Ms,under the 5%label data,its Dice and Hausdorff distance are 79.62%and 15.38%.Under 2%label data,its Dice and Hausdorff distance are 74.03%and 17.05%.Compared with other mainstream methods,the proposed method has better generalization ability.关键词
医学图像分割/元学习/神经架构搜索/域泛化/解耦表示/半监督学习/卷积神经网络/深度学习Key words
medical image segmentation/meta-learning/neural architecture search/domain generalization/disentangle representations/semi-supervised learning/convolutional neural network/deep learning分类
信息技术与安全科学引用本文复制引用
于智洪,李菲菲..基于元学习和神经架构搜索的半监督医学图像分割方法[J].电子科技,2024,37(1):17-23,7.基金项目
上海市高校特聘教授(东方学者)岗位计划(ES2015XX)The Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning(ES2015XX) （东方学者）
