Entity-Aware Medical Image Captioning

Abstract

In order to produce meaningful textual interpretations of intricate clinical images, medical image captioning has become a significant field of study at the nexus of computer vision and natural language processing. Due to the lack of explicit modeling of medical entities, current methods frequently fail to produce descriptions that are both semantically valid and clinically useful, despite notable advances in deep learning and vision language modeling. Typical captioning methods in particular, fall short of being able to retrieve fine grained diagnostic information and maintain semantic consistency with clinical findings as they focus on global features. This paper addresses these limitations by presenting an entity-aware medical image captioning approach which aims to identify and incorporate clinically relevant entities into the caption generation, including but not limited to, diagnostic finding, anatomical structures, or diagnostic characteristics. The proposed method utilizes entity level representations as a means of guiding the captioning process thereby ensuring a tighter semantic consistency between visual modalities and the resultant textual output. Consequently, this leads to more comprehensible, informative and clinically relevant generated reports. Additionally, inclusion of entity awareness can aid the model in effectively understanding the relationships between medical concepts leading to captions more consistent with medical expertise. The results demonstrate that explicit modeling with structured semantic information within vision-language frameworks are crucial and that entity-aware methods have the potential to greatly improve captioning. This work has the ability to help advance health intelligence applications which will serve to better assist clinical decision making, scale the processing of medical images, and facilitate accurate medical documentation.