no code implementations • 20 Nov 2023 • Qingjie Zeng, Yutong Xie, Zilin Lu, Mengkang Lu, Yicheng Wu, Yong Xia
Therefore, in this paper, we introduce a \textbf{Ver}satile \textbf{Semi}-supervised framework (VerSemi) to point out a new perspective that integrates various tasks into a unified model with a broad label space, to exploit more unlabeled data for semi-supervised medical image segmentation.
1 code implementation • 26 Sep 2023 • Qingjie Zeng, Yutong Xie, Zilin Lu, Mengkang Lu, Yong Xia
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation.
no code implementations • CVPR 2023 • Qingjie Zeng, Yutong Xie, Zilin Lu, Yong Xia
In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multi-class and multi-label medical image classification from the point of loss distribution modeling and adversarial training.
Image Classification Semi-supervised Medical Image Classification