  Reflectance confocal microscopy (RCM) is an effective, non-invasive
pre-screening tool for skin cancer diagnosis, but it requires extensive
training and experience to assess accurately. There are few quantitative tools
available to standardize image acquisition and analysis, and the ones that are
available are not interpretable. In this study, we use a recurrent neural
network with attention on convolutional network features. We apply it to
delineate skin strata in vertically-oriented stacks of transverse RCM image
slices in an interpretable manner. We introduce a new attention mechanism
called Toeplitz attention, which constrains the attention map to have a
Toeplitz structure. Testing our model on an expert labeled dataset of 504 RCM
stacks, we achieve 88.17% image-wise classification accuracy, which is the
current state-of-art.
