The success of foundation models such as ChatGPT and AlphaFold has spurred significant
interest in building similar models for electronic medical records (EMRs) to improve patient …

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 …

Conversational generative AI has demonstrated remarkable promise for empowering
biomedical practitioners, but current investigations focus on unimodal text. Multimodal …

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 …

Self-supervised learning in vision--language processing (VLP) exploits semantic alignment
between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the …

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 …

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 …

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 …

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 …

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