Exposing and addressing the fragility of neural networks in digital pathology

Neural networks have achieved impressive results in many medical imaging tasks but often perform substantially worse on out-of-distribution datasets originating from different medical centres or patient cohorts. Evaluating this lack of ability to generalise and address the underlying problem are the two main challenges in developing neural networks intended for clinical practice. In this study, we develop a new method for evaluating neural network models' ability to generalise by generating a large number of distribution-shifted datasets, which can be used to thoroughly investigate their robustness to variability encountered in clinical practice. Compared to external validation, \textit{shifted evaluation} can provide explanations for why neural networks fail on a given dataset, thus offering guidance on how to improve model robustness. With shifted evaluation, we demonstrate that neural networks, trained with state-of-the-art methods, are highly fragile to even small distribution shifts from training data, and in some cases lose all discrimination ability. To address this fragility, we develop an augmentation strategy, explicitly designed to increase neural networks' robustness to distribution shifts. \texttt{StrongAugment} is evaluated with large-scale, heterogeneous histopathology data including five training datasets from two tissue types, 274 distribution-shifted datasets and 20 external datasets from four countries. Neural networks trained with \texttt{StrongAugment} retain similar performance on all datasets, even with distribution shifts where networks trained with current state-of-the-art methods lose all discrimination ability. We recommend using strong augmentation and shifted evaluation to train and evaluate all neural networks intended for clinical practice.