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

By integrating visual and linguistic understanding, visual–language foundation models
(VLFMs) have the great potential to advance the interpretation of medical data, thereby …

Generalizable Face anti-spoofing (FAS) approaches have recently garnered considerable
attention due to their robustness in unseen scenarios. Some recent methods incorporate …

Abstract While Large Language Models (LLMs) excel in world knowledge understanding,
adapting them to specific subfields requires precise adjustments. Due to the model's vast …

Foundation models pretrained on large-scale datasets are revolutionizing the field of
computational pathology (CPath). The generalization ability of foundation models is crucial …

Abstract We present Compound Conditioned ControlNet C3Net a novel generative neural
architecture taking conditions from multiple modalities and synthesizing multimodal contents …

Medical visual question answering (VQA) is a challenging multimodal task, where Vision-
Language Pre-trained (VLP) models can effectively improve the generalization performance …

Many clinical tasks require an understanding of specialized data, such as medical images
and genomics, which is not typically found in general-purpose large multimodal models …

Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to
the scarcity of labeled data in the medical domain. By leveraging paired/unpaired vision and …

Abstract Medical Visual Question Answering (VQA) is an important task in medical multi-
modal Large Language Models (LLMs), aiming to answer clinically relevant questions …