Convolutional Masked Image Modeling for Dense Prediction Tasks on Pathology Images.

This paper studies a convolutional masked image modeling approach for boosting downstream dense prediction tasks on pathology images. Our method is self-supervised, and entails two strategies in sequence. Considering features contained in the pathology images usually have a large spatial span, e.g., glands, we insert [MASK] tokens to the masked regions after the stem layer of the convolutional network for encoding unmasked pixels, which facilitates information propagation through masked regions for reconstructing unmasked pixels. Furthermore, the pathology images contain features that are represented in diverse affine shapes and color spaces. We, therefore, enforce the network to learn the affine and color invariant embedding by imposing transformation constraints between the unmasked image-encoded embedding and reconstruction targets. Our approach is simple but effective. With extensive experiments on standard benchmark datasets, we demonstrate superior transfer learning performance on downstream tasks over past state-of-the-art approaches.