Few-shot Radiology Report Generation for Rare Diseases

摘要

Automatic radiology report generation that interprets medical images and writes their diagnostic reports is in high demand, as the manual written-report can be laborintensive and error-prone. By this context so far, some radiology report generation models have been proposed already which can hardly detect rare diseases accurately due to insufficient training data of such diseases. Radiology report generation task is therefore severely challenged while involving the rare disease. To tackle this problem, we propose a few-shot Radiology report Generation model, namely RareGen, assembled with two components for better semantic representations learning which can benefit rare disease detection and their diagnosis report generation. Specifically, a few-shot learning generative network is introduced for generating artificial medical instances for rare diseases. Moreover, a disease graph convolution is proposed to model and strengthen the intrinsic correlations among diseases, which allows knowledge transfer from regular diseases to those rare diseases. To the best of our knowledge, this is the first study that focuses on rare disease diagnosis report generation from radiology data. Extensive experiments are conducted to demonstrate the effectiveness of our model.