Danran Bu, Chun-Qing Zhang, Jing-Dong Liu, Zhe Han, Xiang Wang, Zhijian Huang
Mental health literacy, mental health experiences and help-seeking behaviours of Chinese elite athletes: a qualitative study
Frontiers in public health
advancements clinical treatment increasingly constrained limitations supervised learning techniques depend heavily large volumes annotated data annotation process costly also demands substantial time clinical specialists addressing issue introduce s4mi selfsupervision semisupervision medical imaging pipeline novel approach leverages advancements selfsupervised semisupervised learning techniques engage auxiliary tasks require labeling thus simplifying scaling machine supervision compared fullysupervised methods study benchmarks techniques three distinct medical imaging datasets evaluate effectiveness classification segmentation tasks notably observed selfsupervised learning significantly surpassed performance supervised methods classification evaluated datasets remarkably semisupervised approach demonstrated superior outcomes segmentation outperforming fullysupervised methods using 50 fewer labels across datasets line commitment contributing scientific community made s4mi code openly accessible allowing broader application development methods code accessed httpsgithubcompranavsinghps1s4mi