Grounding Chest X-Ray Visual Question Answering with Generated Radiology Reports

TL;DR

This paper introduces a unified approach to Chest X-ray VQA that leverages radiology reports to improve answer accuracy for both single-image and image-difference questions, achieving state-of-the-art results.

Contribution

It extends prior work by integrating radiology report generation into the VQA process, enhancing model performance for temporal change detection and abnormality identification.

Findings

Incorporating radiology reports improves VQA accuracy.

The model effectively handles both single-image and image-difference questions.

Achieved state-of-the-art results on Medical-Diff-VQA dataset.

Abstract

We present a novel approach to Chest X-ray (CXR) Visual Question Answering (VQA), addressing both single-image image-difference questions. Single-image questions focus on abnormalities within a specific CXR ("What abnormalities are seen in image X?"), while image-difference questions compare two longitudinal CXRs acquired at different time points ("What are the differences between image X and Y?"). We further explore how the integration of radiology reports can enhance the performance of VQA models. While previous approaches have demonstrated the utility of radiology reports during the pre-training phase, we extend this idea by showing that the reports can also be leveraged as additional input to improve the VQA model's predicted answers. First, we propose a unified method that handles both types of questions and auto-regressively generates the answers. For single-image questions, the model is provided with a single CXR. For image-difference questions, the model is provided with two CXRs from the same patient, captured at different time points, enabling the model to detect and describe temporal changes. Taking inspiration from 'Chain-of-Thought reasoning', we demonstrate that performance on the CXR VQA task can be improved by grounding the answer generator module with a radiology report predicted for the same CXR. In our approach, the VQA model is divided into two steps: i) Report Generation (RG) and ii) Answer Generation (AG). Our results demonstrate that incorporating predicted radiology reports as evidence to the AG model enhances performance on both single-image and image-difference questions, achieving state-of-the-art results on the Medical-Diff-VQA dataset.