FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
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Automatic medical report generation (MRG) towards describing life-threatening lesions from given medical images, such as Chest X-ray and Fundus Fluorescein Angiography (FFA), has been a long-standing research topic in machine learning and automatic medical diagnosis fields. However, existing MRG benchmarks only provide medical images and free-text reports without explainable annotations and reliable evaluation tools, hindering the current research advances from two aspects: First, existing methods can only predict reports without accurate explanation, undermining the trustworthiness of the diagnostic methods; Second, the comparison between predicted reports from MRG methods is unreliable based on the natural language generation (NLG) metrics. To address these issues, we propose an explainable and reliable MRG benchmark based on FFA Images and Reports (FFA-IR). Specifically, our FFA-IR dataset is featured from the following aspects: 1) Large-scale medical dataset. FFA-IR collects 766 reports along with 47,247 FFA images from clinical practice. 2) Explainable annotation. FFA-IR annotates 46 categories of lesions with a total of 12,166 regions. 3) Bilingual reports. FFA-IR provides both English and Chinese reports for each case. We hope that our FFA-IR can significantly advance research from both vision-and-language and medicine fields and improve the conventional retinal disease diagnosis procedure.
面向从医学图像（如胸部X光片、荧光素眼底血管造影（Fundus Fluorescein Angiography, FFA）图像）中描述危及生命病变的自动医学报告生成（Medical Report Generation, MRG）任务，长期以来都是机器学习与自动医学诊断领域的研究热点。然而，现有MRG基准数据集仅提供医学图像与自由文本报告，缺乏可解释性标注与可靠的评估工具，从两方面阻碍了当前研究的推进：其一，现有方法仅能生成报告却无法提供精准解释，削弱了诊断方法的可信度；其二，基于自然语言生成（Natural Language Generation, NLG）指标对MRG模型生成的报告进行对比，结果并不可靠。 为解决上述问题，我们提出了一款基于FFA图像与报告的可解释、可靠的MRG基准数据集（FFA-IR）。具体而言，本FFA-IR数据集具有以下特点：1）大规模医学数据集：FFA-IR从临床实践中收集了766份报告与47247张FFA图像；2）可解释性标注：FFA-IR针对46类病变共12166个区域完成了标注；3）双语报告：每一例样本均提供英文与中文双语报告。我们期望本FFA-IR数据集能够显著推动视觉语言与医学两大领域的研究进展，并优化传统视网膜疾病的诊断流程。
