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.

本研究解决了医疗报告生成中的传统序列生成方法不足的问题，提出将其视为多标签分类问题的新方法。通过引入基于BLIP的报告生成框架，利用分类技术捕捉放射学节点，实现了报告生成的有效性和多方面准确分类。结果表明，该方法在两个基准数据集上实现了最先进的表现，展现了重新审视传统任务的重要性。

医疗报告生成是一个多标签分类问题