Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology

TL;DR

This paper introduces a benchmark to evaluate the robustness of deep neural networks against common corruptions in digital pathology images, revealing significant accuracy drops and unreliable confidence estimates under corruption.

Contribution

It provides an easy-to-use benchmark with corrupted images and new metrics to assess model robustness and confidence in digital pathology applications.

Findings

Deep neural networks' accuracy drops significantly on corrupted images.

Confidence estimation becomes unreliable under image corruption.

Replacing validation sets with the benchmark improves error correlation.

Abstract

When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted images are generated by injecting nine types of common corruptions into validation images. Besides, two classification and one ranking metrics are designed to evaluate the prediction and confidence performance under corruption. Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation. Our codes are available on https://github.com/superjamessyx/robustness_benchmark.