Every year physicians face an increasing demand of image-based diagnosis from patients, a
problem that can be addressed with recent artificial intelligence methods. In this context, we …

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 …

Recently, chest X-ray report generation, which aims to automatically generate descriptions
of given chest X-ray images, has received growing research interests. The key challenge of …

Neural image-to-text radiology report generation systems offer the potential to improve
radiology reporting by reducing the repetitive process of report drafting and identifying …

Diagnostic captioning (DC) concerns the automatic generation of a diagnostic text from a set
of medical images of a patient collected during an examination. DC can assist …

Automated generation of clinically accurate radiology reports can improve patient care.
Previous report generation methods that rely on image captioning models often generate …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of powerful models for various pathology tasks across a diverse array of …

We propose CXR-RePaiR: a retrieval-based radiology report generation approach using a
pre-trained contrastive language-image model. Our method generates clinically accurate …

Natural Language Generation (NLG) has made great progress in recent years due to the
development of deep learning techniques such as pre-trained language models. This …

Radiology report generation aims at generating descriptive text from radiology images
automatically, which may present an opportunity to improve radiology reporting and …