Medical Report Generation Is A Multi-label Classification Problem

TL;DR

This paper redefines medical report generation as a multi-label classification task, utilizing knowledge graph nodes and a BLIP-based framework to improve accuracy and achieve state-of-the-art results.

Contribution

It introduces a novel classification-based framework for medical report generation, leveraging knowledge graph nodes and a BLIP model to enhance performance.

Findings

Achieved state-of-the-art performance on benchmark datasets.

Outperformed traditional sequence generation methods.

Validated the effectiveness of classification approach.

Abstract

Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence generation problem, relying on vision-and-language techniques to generate coherent and contextually relevant reports. However, in this paper, we propose a novel perspective: rethinking medical report generation as a multi-label classification problem. By framing the task this way, we leverage the radiology nodes from the commonly used knowledge graph, which can be better captured through classification techniques. To verify our argument, we introduce a novel report generation framework based on BLIP integrated with classified key nodes, which allows for effective report generation with accurate classification of multiple key aspects within the medical images. This approach not only simplifies the report generation process but also significantly enhances performance metrics. Our extensive experiments demonstrate that leveraging key nodes can achieve state-of-the-art (SOTA) performance, surpassing existing approaches across two benchmark datasets. The results underscore the potential of re-envisioning traditional tasks with innovative methodologies, paving the way for more efficient and accurate medical report generation.