Devdiscourse

Self-supervised AI can unlock hidden disease patterns

Published as an arXiv preprint, the paper details how unsupervised and self-supervised AI models are matching or surpassing ...
IEEE

Unsupervised Domain Adaptation for Medical Image Segmentation by Disentanglement Learning and Self-Training

Abstract: Unsupervised domain adaption (UDA), which aims to enhance the segmentation performance of deep models on unlabeled data, has recently drawn much attention. In this paper, we propose a novel ...
IEEE

CiSeg: Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation via Causal Intervention

Abstract: Unsupervised domain adaptation (UDA) addresses the domain shift problem by transferring knowledge from labeled source domain data (e.g. CT) to unlabeled target domain data (e.g. MRI). While ...
C&EN

Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In recent AI-driven disease diagnosis, the success of models has depended mainly on ...