基于元学习和神经架构搜索的半监督医学图像分割方法OA

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.