Self-supervision paper from arxiv for histopathology CV.

Authors draw inspiration from the process of how histopathologists tend to review the images, and how those images are stored. Histopathology images are multiscale slices of enormous size (tens of thousands pixels by one side), and area experts constantly move through different levels of magnification to keep in mind both fine and coarse structures of the tissue.

Therefore, in this paper the loss is proposed to capture relation between different magnification levels. Authors propose to train network to order concentric patches by their magnification level. They organise it as the classification task — network to predict id of the order permutation instead of predicting order itself.

Also, authors proposed specific architecture for this task and appended self-training procedure, as it was shown to boost results even after pre-training.

All this allows them to reach quality increase even in high-data regime.

My description of the architecture and loss expanded here.
Source of the work here.