FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark

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.