Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

The rapid development of diagnostic technologies in healthcare is leading to higher
requirements for physicians to handle and integrate the heterogeneous, yet complementary …

A long-running goal of the clinical NLP community is the extraction of important variables
trapped in clinical notes. However, roadblocks have included dataset shift from the general …

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 …

In recent years, the growing number of medical imaging studies is placing an ever-
increasing burden on radiologists. Deep learning provides a promising solution for …

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 …

In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing
radiology reports is a heavy burden for radiologists. To this end, we present an automatic …

Medical contrastive vision-language pretraining has shown great promise in many
downstream tasks, such as data-efficient/zero-shot recognition. Current studies pretrain the …

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

We propose and demonstrate a representation learning approach by maximizing the mutual
information between local features of images and text. The goal of this approach is to learn …