  In clinical scenarios, multiple medical images with different views are
usually generated at the same time, and they have high semantic consistency.
However, the existing medical report generation methods cannot exploit the rich
multi-view mutual information of medical images. Therefore, in this work, we
propose the first multi-view medical report generation model, called MvCo-DoT.
Specifically, MvCo-DoT first propose a multi-view contrastive learning (MvCo)
strategy to help the deep reinforcement learning based model utilize the
consistency of multi-view inputs for better model learning. Then, to close the
performance gaps of using multi-view and single-view inputs, a domain transfer
network is further proposed to ensure MvCo-DoT achieve almost the same
performance as multi-view inputs using only single-view inputs.Extensive
experiments on the IU X-Ray public dataset show that MvCo-DoT outperforms the
SOTA medical report generation baselines in all metrics.
