Expert Knowledge-Aware Image Difference Graph Representation Learning
  for Difference-Aware Medical Visual Question Answering
Xinyue Hu Lin Gu Qiyuan An Mengliang Zhang Liangchen Liu Kazuma Kobayashi Tatsuya Harada Ronald M. Summers Yingying Zhu
To contribute to automating the medical vision-language model, we propose a
novel Chest-Xray Difference Visual Question Answering (VQA) task. Given a pair
of main and reference images, this task attempts to answer several questions on
both diseases and, more importantly, the differences between them. This is
consistent with the radiologist's diagnosis practice that compares the current
image with the reference before concluding the report. We collect a new
dataset, namely MIMIC-Diff-VQA, including 700,703 QA pairs from 164,324 pairs
of main and reference images. Compared to existing medical VQA datasets, our
questions are tailored to the Assessment-Diagnosis-Intervention-Evaluation
treatment procedure used by clinical professionals. Meanwhile, we also propose
a novel expert knowledge-aware graph representation learning model to address
this task. The proposed baseline model leverages expert knowledge such as
anatomical structure prior, semantic, and spatial knowledge to construct a
multi-relationship graph, representing the image differences between two images
for the image difference VQA task. The dataset and code can be found at
https://github.com/Holipori/MIMIC-Diff-VQA. We believe this work would further
push forward the medical vision language model.