Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment

TL;DR

This paper introduces a novel generative model for automatic radiology report generation from chest X-ray images, combining multi-view image fusion and medical concept enrichment to improve accuracy and interpretability.

Contribution

It proposes a multi-view image fusion and medical concept enrichment approach, enhancing report generation accuracy with a pretrained encoder and attention mechanisms.

Findings

Achieves state-of-the-art performance on Indiana University Chest X-Ray dataset.

Effectively recognizes 14 radiographic observations and extracts key medical concepts.

Improves report relevance and correctness through multi-view and concept-based fusion.

Abstract

Generating radiology reports is time-consuming and requires extensive expertise in practice. Therefore, reliable automatic radiology report generation is highly desired to alleviate the workload. Although deep learning techniques have been successfully applied to image classification and image captioning tasks, radiology report generation remains challenging in regards to understanding and linking complicated medical visual contents with accurate natural language descriptions. In addition, the data scales of open-access datasets that contain paired medical images and reports remain very limited. To cope with these practical challenges, we propose a generative encoder-decoder model and focus on chest x-ray images and reports with the following improvements. First, we pretrain the encoder with a large number of chest x-ray images to accurately recognize 14 common radiographic observations, while taking advantage of the multi-view images by enforcing the cross-view consistency. Second, we synthesize multi-view visual features based on a sentence-level attention mechanism in a late fusion fashion. In addition, in order to enrich the decoder with descriptive semantics and enforce the correctness of the deterministic medical-related contents such as mentions of organs or diagnoses, we extract medical concepts based on the radiology reports in the training data and fine-tune the encoder to extract the most frequent medical concepts from the x-ray images. Such concepts are fused with each decoding step by a word-level attention model. The experimental results conducted on the Indiana University Chest X-Ray dataset demonstrate that the proposed model achieves the state-of-the-art performance compared with other baseline approaches.