Multimodal Generation of Radiology Reports using Knowledge-Grounded Extraction of Entities and Relations

Abstract

Automated reporting has the potential to assist radiologists with the time-consuming procedure of generating text radiology reports. Most existing approaches generate the report directly from the radiology image, however we observe that the resulting reports exhibit realistic style but lack clinical accuracy. Therefore, we propose a two-step pipeline that subdivides the problem into factual triple extraction followed by free-text report generation. The first step comprises supervised extraction of clinically relevant structured information from the image, expressed as triples of the form (entity1, relation, entity2). In the second step, these triples are input to condition the generation of the radiology report. In particular, we focus our work on Chest X-Ray (CXR) radiology report generation. The proposed framework shows state-of-the-art results on the MIMIC-CXR dataset according to most of the standard text generation metrics that we employ (BLEU, METEOR, ROUGE) and to clinical accuracy metrics (recall, precision and F1 assessed using the CheXpert labeler), also giving a 23% reduction in the total number of errors and a 29% reduction in critical clinical errors as assessed by expert human evaluation. In future, this solution can easily integrate more advanced model architectures - to both improve the triple extraction and the report generation - and can be applied to other complex image captioning tasks, such as those found in the medical domain.