Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

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

Multi-modal data abounds in biomedicine, such as radiology images and reports.
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …

Learning visual representations of medical images (eg, X-rays) is core to medical image
understanding but its progress has been held back by the scarcity of human annotations …

While multi-modal foundation models pre-trained on large-scale data have been successful
in natural language understanding and vision recognition, their use in medical domains is …

Contrastive pretraining on parallel image-text data has attained great success in vision-
language processing (VLP), as exemplified by CLIP and related methods. However, prior …

Learning medical visual representations directly from paired radiology reports has become
an emerging topic in representation learning. However, existing medical image-text joint …

In this paper, we consider enhancing medical visual-language pre-training (VLP) with
domain-specific knowledge, by exploiting the paired image-text reports from the radiological …

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

Deep learning has been widely used in medical image segmentation and other aspects.
However, the performance of existing medical image segmentation models has been limited …