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Reflectance confocal microscopy (RCM) is an effective, non-invasivepre-screening tool for skin cancer diagnosis, but it requires extensivetraining and experience to assess accurately. There are few quantitative toolsavailable to standardize image acquisition and analysis, and the ones that areavailable are not interpretable. In this study, we use a recurrent neuralnetwork with attention on convolutional network features. We apply it todelineate skin strata in vertically-oriented stacks of transverse RCM imageslices in an interpretable manner. We introduce a new attention mechanismcalled Toeplitz attention, which constrains the attention map to have aToeplitz structure. Testing our model on an expert labeled dataset of 504 RCMstacks, we achieve 88.17% image-wise classification accuracy, which is thecurrent state-of-art.

Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention