Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
This work addresses annotation inefficiency for medical AI in histopathology, offering incremental improvements in data utilization and uncertainty quantification.
The paper tackles the problem of high annotation costs in histopathology by using large unlabeled datasets for pre-training and fine-tuning on limited annotations, achieving state-of-the-art patch-level classification with only 1-10% of annotations and improving whole-slide image classification with weak supervision.
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.