Contrastive Deep Encoding Enables Uncertainty-aware
  Machine-learning-assisted Histopathology
Nirhoshan Sivaroopan Chamuditha Jayanga Chalani Ekanayake Hasindri Watawana Jathurshan Pradeepkumar Mithunjha Anandakumar Ranga Rodrigo Chamira U. S. Edussooriya Dushan N. Wadduwage
Deep neural network models can learn clinically relevant features from
millions of histopathology images. However generating high-quality annotations
to train such models for each hospital, each cancer type, and each diagnostic
task is prohibitively laborious. On the other hand, terabytes of training data
-- while lacking reliable annotations -- are readily available in the public
domain in some cases. In this work, we explore how these large datasets can be
consciously utilized to pre-train deep networks to encode informative
representations. We then fine-tune our pre-trained models on a fraction of
annotated training data to perform specific downstream tasks. We show that our
approach can reach the state-of-the-art (SOTA) for patch-level classification
with only 1-10% randomly selected annotations compared to other SOTA
approaches. Moreover, we propose an uncertainty-aware loss function, to
quantify the model confidence during inference. Quantified uncertainty helps
experts select the best instances to label for further training. Our
uncertainty-aware labeling reaches the SOTA with significantly fewer
annotations compared to random labeling. Last, we demonstrate how our
pre-trained encoders can surpass current SOTA for whole-slide image
classification with weak supervision. Our work lays the foundation for data and
task-agnostic pre-trained deep networks with quantified uncertainty.