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Medical imaging is frequently used in clinical practice and trials fordiagnosis and treatment. Writing imaging reports is time-consuming and can beerror-prone for inexperienced radiologists. Therefore, automatically generatingradiology reports is highly desired to lighten the workload of radiologists andaccordingly promote clinical automation, which is an essential task to applyartificial intelligence to the medical domain. In this paper, we propose togenerate radiology reports with memory-driven Transformer, where a relationalmemory is designed to record key information of the generation process and amemory-driven conditional layer normalization is applied to incorporating thememory into the decoder of Transformer. Experimental results on two prevailingradiology report datasets, IU X-Ray and MIMIC-CXR, show that our proposedapproach outperforms previous models with respect to both language generationmetrics and clinical evaluations. Particularly, this is the first workreporting the generation results on MIMIC-CXR to the best of our knowledge.Further analyses also demonstrate that our approach is able to generate longreports with necessary medical terms as well as meaningful image-text attentionmappings.

Generating Radiology Reports via Memory-driven Transformer