Continual learning (CL) aims to help deep neural networks learn new knowledge while
retaining what has been learned. Owing to their powerful generalizability, pre-trained vision …

Pre-trained vision-language models (VLMs) have shown impressive results in various visual
classification tasks. However, we often fail to fully unleash their potential when adapting …

Medical foundation models are gaining prominence in the medical community for their ability
to derive general representations from extensive collections of medical image-text pairs …

Vision-Language Models (VLMs) trained via contrastive learning have achieved notable
success in natural image tasks. However, their application in the medical domain remains …

Vision-language foundation models (such as CLIP) have recently shown their power in
transfer learning, owing to large-scale image-text pre-training. However, target domain data …

Pre-trained vision-language models like CLIP have remarkably adapted to various
downstream tasks. Nonetheless, their performance heavily depends on the specificity of the …

Recent advancements in vision-language models (VLMs), such as CLIP, have demonstrated
substantial success in self-supervised representation learning for vision tasks. However …

In the era of foundation models, CLIP has emerged as a powerful tool for aligning text and
visual modalities into a common embedding space. However, the alignment objective used …

Recent advancements in medical vision-language pre-training (MedVLP) have significantly
enhanced zero-shot medical vision tasks such as image classification by leveraging large …

This study explores the concept of cross-disease transferability (XDT) in medical imaging,
focusing on the potential of binary classifiers trained on one disease to perform zero-shot …