Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation  

作　　者：Hengyang Liu Yang Yuan Pengcheng Ren Chengyun Song Fen Luo 

机构地区：[1]School of Computer Science and Engineering,Chongqing University of Technology,Chongqing,400054,China [2]College of Artificial Intelligence,Chongqing Technology and Business University,Chongqing,400067,China

出　　处：《Computers, Materials & Continua》2025年第1期543-560,共18页计算机、材料和连续体（英文）

基　　金：supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).

摘　　要：Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

关 键 词：SEMI-SUPERVISED medical image segmentation contrastive learning stochastic augmented 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]