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

The scarcity of data presents a critical obstacle to the efficacy of medical vision-language pre-
training (VLP). A potential solution lies in the combination of datasets from various language …

Deep neural networks have been integrated into the whole clinical decision procedure
which can improve the efficiency of diagnosis and alleviate the heavy workload of …

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

A large-scale image-text pair dataset has greatly contributed to the development of vision-
language pre-training (VLP) models, which enable zero-shot or few-shot classification …

In medical Vision-Language Pre-training (VLP), significant work focuses on extracting text
and image features from clinical reports and medical images. Yet, existing methods may …

Recently, multi-modal vision-language foundation models have gained significant attention
in the medical field. While these models offer great opportunities, they still face crucial …

Abstract The advancement of Zero-Shot Learning in the medical domain has been driven
forward by using pre-trained models on large-scale image-text pairs focusing on image-text …

Abstract Language-supervised pretraining has proven to be a valuable method for extracting
semantically meaningful features from images, serving as a foundational element in …

Medical report generation generates the corresponding report according to the given
radiology image, which has been attracting increasing research interest. However, existing …