  Training neural networks with high-quality pixel-level annotation in
histopathology whole-slide images (WSI) is an expensive process due to
gigapixel resolution of WSIs. However, recent advances in self-supervised
learning have shown that highly descriptive image representations can be
learned without the need for annotations. We investigate the application of the
recent Hierarchical Image Pyramid Transformer (HIPT) model for the specific
task of classification of colorectal biopsies and polyps. After evaluating the
effectiveness of TCGA-learned features in the original HIPT model, we
incorporate colon biopsy image information into HIPT's pretraining using two
distinct strategies: (1) fine-tuning HIPT from the existing TCGA weights and
(2) pretraining HIPT from random weight initialization. We compare the
performance of these pretraining regimes on two colorectal biopsy
classification tasks: binary and multiclass classification.
