Assistive Diagnosis in Opthalmology Using Deep Learning-Based Image Retrieval
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Image-based diagnosis of the human eye is crucial for the early detection of several diseases in ophthalmology. In this work, we investigate the possibility to use image retrieval to support the diagnosis of diabetic retinopathy. To this end, we evaluate different feature learning techniques. In particular, we evaluate the performance of cost functions specialized for metric learning, namely, contrastive loss, triplet loss and histogram loss, and compare them with the classification crossentropy loss. Additionally, we train the network on images graded by diabetic retinopathy severity and transfer the knowledge learned, to retrieve images that are graded by diabetic macular edema severity and evaluate our algorithm on three different datasets. For the task of detecting referable/non-referable diabetic retinopathy, we achieve a sensitivity of 0.84 and specificity of 0.88 on the Kaggle dataset using histogram loss. On the Messidor dataset, we achieve a sensitivity and specificity score of 0.79 and 0.84, respectively.
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