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 …

The automatic generation of radiology reports has the potential to assist radiologists in the
time-consuming task of report writing. Existing methods generate the full report from image …

Learning from positive and unlabeled data or PU learning is the setting where a learner only
has access to positive examples and unlabeled data. The assumption is that the unlabeled …

Radiology report generation (RRG) aims to automatically generate free-text descriptions
from clinical radiographs, eg, chest X-Ray images. RRG plays an essential role in promoting …

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 …

Abstract Electronic Health Records (EHRs), which contain patients' medical histories in
various multi-modal formats, often overlook the potential for joint reasoning across imaging …

We present a radiology-specific multimodal model for the task for generating radiological
reports from chest X-rays (CXRs). Our work builds on the idea that large language model (s) …

To contribute to automating the medical vision-language model, we propose a novel Chest-
Xray Different Visual Question Answering (VQA) task. Given a pair of main and reference …

We present RadGraph2, a novel dataset for extracting information from radiology reports that
focuses on capturing changes in disease state and device placement over time. We …

The goal of automatic report generation is to generate a clinically accurate and coherent
phrase from a single given X-ray image, which could alleviate the workload of traditional …