  Tissue phenotyping is a fundamental task in learning objective
characterizations of histopathologic biomarkers within the tumor-immune
microenvironment in cancer pathology. However, whole-slide imaging (WSI) is a
complex computer vision in which: 1) WSIs have enormous image resolutions with
precludes large-scale pixel-level efforts in data curation, and 2) diversity of
morphological phenotypes results in inter- and intra-observer variability in
tissue labeling. To address these limitations, current efforts have proposed
using pretrained image encoders (transfer learning from ImageNet,
self-supervised pretraining) in extracting morphological features from
pathology, but have not been extensively validated. In this work, we conduct a
search for good representations in pathology by training a variety of
self-supervised models with validation on a variety of weakly-supervised and
patch-level tasks. Our key finding is in discovering that Vision Transformers
using DINO-based knowledge distillation are able to learn data-efficient and
interpretable features in histology images wherein the different attention
heads learn distinct morphological phenotypes. We make evaluation code and
pretrained weights publicly-available at:
https://github.com/Richarizardd/Self-Supervised-ViT-Path.
