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

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 …

Medical images and radiology reports are essential for physicians to diagnose medical
conditions. However, the vast diversity and cross-source heterogeneity inherent in these …

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 …

This study evaluates the potential of the “Segment Anything Model”(SAM) as a robust
alternative for medical imaging segmentation in a zero-shot learning context. We evaluate …

Foundation models (FMs) are large-scale deep-learning models trained on extensive
datasets using self-supervised techniques. These models serve as a base for various …

Medical image analysis powered by deep learning computer vision models has achieved
significant advancements in the past decade. Deep learning models have demonstrated …

With the development of Computer-Aided Diagnosis (CAD) systems and Deep Learning
(DL) technologies, significant advancements have been made in the automatic analysis of …