Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology

• URL: http://arxiv.org/abs/2309.07113v1
• Date: Wed, 13 Sep 2023 17:37:19 GMT
• Title: Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
• Authors: Nirhoshan Sivaroopan, Chamuditha Jayanga, Chalani Ekanayake, Hasindri Watawana, Jathurshan Pradeepkumar, Mithunjha Anandakumar, Ranga Rodrigo, Chamira U. S. Edussooriya, and Dushan N. Wadduwage
• Abstract summary: terabytes of training data can be consciously utilized to pre-train deep networks to encode informative representations. We show that our approach can reach the state-of-the-art (SOTA) for patch-level classification with only 1-10% randomly selected annotations.
• Score: 6.548275341067594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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