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Automated radiology report generation aims at automatically generating adetailed description of medical images, which can greatly alleviate theworkload of radiologists and provide better medical services to remote areas.Most existing works pay attention to the holistic impression of medical images,failing to utilize important anatomy information. However, in actual clinicalpractice, radiologists usually locate important anatomical structures, and thenlook for signs of abnormalities in certain structures and reason the underlyingdisease. In this paper, we propose a novel framework AGFNet to dynamically fusethe global and anatomy region feature to generate multi-grained radiologyreport. Firstly, we extract important anatomy region features and globalfeatures of input Chest X-ray (CXR). Then, with the region features and theglobal features as input, our proposed self-adaptive fusion gate module coulddynamically fuse multi-granularity information. Finally, the captioninggenerator generates the radiology reports through multi-granularity features.Experiment results illustrate that our model achieved the state-of-the-artperformance on two benchmark datasets including the IU X-Ray and MIMIC-CXR.Further analyses also prove that our model is able to leverage themulti-grained information from radiology images and texts so as to helpgenerate more accurate reports.

Self adaptive global-local feature enhancement for radiology report generation