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 …

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 …

Contrastive Language-Image Pre-training (CLIP), a straightforward yet effective pre-training
paradigm, successfully introduces semantic-rich text supervision to vision models and has …

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

Medical vision-language models enable co-learning and integrating features from medical
imaging and clinical text. However, these models are not easy to train and the latent …

Medical vision-and-language pre-training (Med-VLP) has shown promising improvements
on many downstream medical tasks owing to its applicability to extracting generic …

In the field of medical Vision-Language Pre-training (VLP), significant efforts have been
devoted to deriving text and image features from both clinical reports and associated …

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 …

Electron microscopy (EM) images are notoriously challenging to segment due to their
complex structures and lack of effective annotations. Fortunately, large-scale self-supervised …

Objective Computer-assisted diagnostic and prognostic systems of the future should be
capable of simultaneously processing multimodal data. Multimodal deep learning (MDL) …