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Automatically generating medical reports for retinal images is one of thepromising ways to help ophthalmologists reduce their workload and improve workefficiency. In this work, we propose a new context-driven encoding network toautomatically generate medical reports for retinal images. The proposed modelis mainly composed of a multi-modal input encoder and a fused-feature decoder.Our experimental results show that our proposed method is capable ofeffectively leveraging the interactive information between the input image andcontext, i.e., keywords in our case. The proposed method creates more accurateand meaningful reports for retinal images than baseline models and achievesstate-of-the-art performance. This performance is shown in several commonlyused metrics for the medical report generation task: BLEU-avg (+16%), CIDEr(+10.2%), and ROUGE (+8.6%).

Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"