  Generating medical reports for X-ray images presents a significant challenge,
particularly in unpaired scenarios where access to paired image-report data for
training is unavailable. Previous works have typically learned a joint
embedding space for images and reports, necessitating a specific labeling
schema for both. We introduce an innovative approach that eliminates the need
for consistent labeling schemas, thereby enhancing data accessibility and
enabling the use of incompatible datasets. This approach is based on
cycle-consistent mapping functions that transform image embeddings into report
embeddings, coupled with report auto-encoding for medical report generation.
Our model and objectives consider intricate local details and the overarching
semantic context within images and reports. This approach facilitates the
learning of effective mapping functions, resulting in the generation of
coherent reports. It outperforms state-of-the-art results in unpaired chest
X-ray report generation, demonstrating improvements in both language and
clinical metrics.
