Large language models (LLMs), such as ChatGPT, have received substantial attention due
to their capabilities for understanding and generating human language. While there has …

Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …

The utilization of large language models (LLMs) for Healthcare has generated both
excitement and concern due to their ability to effectively respond to free-text queries with …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …

In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA),
which is crucial in efficiently interpreting medical images with vital clinic-relevant information …

Contrastive learning based vision-language joint pre-training has emerged as a successful
representation learning strategy. In this paper, we present a prototype representation …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

In this study, we aim to initiate the development of Radiology Foundation Model, termed as
RadFM. We consider the construction of foundational models from the perspectives of data …

Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

Automated medical image analysis systems often require large amounts of training data with
high quality labels, which are difficult and time consuming to generate. This paper …