A Self-Guided Framework for Radiology Report Generation

TL;DR

This paper introduces a self-guided framework for radiology report generation that leverages unsupervised and supervised learning to improve accuracy and detail without relying on annotated disease labels.

Contribution

The proposed SGF framework uniquely combines unsupervised and supervised methods to extract fine-grained visual features and enhance report quality without extra disease labels.

Findings

SGF outperforms state-of-the-art methods in report accuracy

It effectively captures fine-grained visual details

The framework improves report length and detail

Abstract

Automatic radiology report generation is essential to computer-aided diagnosis. Through the success of image captioning, medical report generation has been achievable. However, the lack of annotated disease labels is still the bottleneck of this area. In addition, the image-text data bias problem and complex sentences make it more difficult to generate accurate reports. To address these gaps, we pre-sent a self-guided framework (SGF), a suite of unsupervised and supervised deep learning methods to mimic the process of human learning and writing. In detail, our framework obtains the domain knowledge from medical reports with-out extra disease labels and guides itself to extract fined-grain visual features as-sociated with the text. Moreover, SGF successfully improves the accuracy and length of medical report generation by incorporating a similarity comparison mechanism that imitates the process of human self-improvement through compar-ative practice. Extensive experiments demonstrate the utility of our SGF in the majority of cases, showing its superior performance over state-of-the-art meth-ods. Our results highlight the capacity of the proposed framework to distinguish fined-grained visual details between words and verify its advantage in generating medical reports.