3 accepted papers
Medical vision-language pre-training (VLP) offers significant potential for advancing medical image understanding by leveraging paired image-report data. However, existing methods are limited by False Negatives (FaNe) induced by semantically similar texts and insufficient fine-grained cross-modal al
In order to obtain good medical image segmentation results, existing studies usually extract multi-scale features or design special attention to obtain global and local information of images. However, the above methods are very cumbersome or have a high computational burden. Theoretically, global an…
Recently, contrastive learning has shown great potential in medical image segmentation. Due to the lack of expert annotations, however, it is challenging to apply contrastive learning in semi-supervised scenes. To solve this problem, we propose a novel uncertainty-guided pixel contrastive learning m…