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Automated diagnosis using deep neural networks can help ophthalmologistsdetect the blinding eye disease wet Age-related Macular Degeneration (AMD).Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal ChoroidalVessels (PCV). However, due to the difficulty in data collection and thesimilarity between images, most studies have only achieved the coarse-grainedclassification of wet-AMD rather than a finer-grained one of wet-AMD subtypes.To solve this issue, in this paper we propose a Knowledge-driven Fine-grainedWet-AMD Classification Model (KFWC), to classify fine-grained diseases withinsufficient data. With the introduction of a priori knowledge of 10 lesionsigns of input images into the KFWC, we aim to accelerate the KFWC by means ofmulti-label classification pre-training, to locate the decisive image featuresin the fine-grained disease classification task and therefore achieve betterclassification. Simultaneously, the KFWC can also provide good interpretabilityand effectively alleviate the pressure of data collection and annotation in thefield of fine-grained disease classification for wet-AMD. The experimentsdemonstrate the effectiveness of the KFWC which reaches 99.71% in AU-ROCscores, and its considerable improvements over the data-driven w/o Knowledgeand ophthalmologists, with the rates of 6.69% over the strongest baseline and4.14% over ophthalmologists.

KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD