Addressing Data Bias Problems for Chest X-ray Image Report Generation
This addresses bias in medical report generation for doctors, but it is incremental as it builds on existing hierarchical LSTM approaches.
The paper tackled data bias in chest X-ray report generation by separating abnormal and normal sentence generation using two word LSTMs in a hierarchical model, resulting in increased sentence distinctiveness compared to BLEU scores.
Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using two different word LSTMs in a hierarchical LSTM model. We conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation.