  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.
