Radiology report generation with medical knowledge and multilevel image-report alignment: A new method and its verification

Abstract

Medical report generation is an integral part of computer-aided diagnosis aimed at reducing the workload of radiologists and physicians and alerting them of misdiagnosis risks. In general, medical report generation is an image captioning task. Since medical reports have long sequences with data bias, the existing medical report generation models lack medical knowledge and ignore the interaction alignment between the two modalities of reports and images. The current paper attempts to mitigate these deficiencies by proposing an approach based on knowledge enhancement with multilevel alignment (MKMIA). To this end, it includes a knowledge enhancement (MKE) module and a multilevel alignment module (MIRA). Specifically, the MKE deals with general medical knowledge (MK) and historical knowledge (HK) obtained via data training. The general knowledge is embedded in the form of a dictionary with characteristic organs (referred to as Key) and organ aliases, disease symptoms, etc. (referred to as Value). It provides explicit exception candidates to mitigate data bias. Historical knowledge ensures the comparison of similar cases to provide a better diagnosis. MIRA furnishes coarse-to-fine multilevel alignment, reducing the gap between image and text features, improving the knowledge enhancement module’s performance, and facilitating the generation of lengthy reports. Experimental results on two radiology report datasets (i.e., IU X-ray and MIMIC-CXR) proved the effectiveness of the proposed approach, achieving state-of-the-art performance.