Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
This work addresses the need for robust diagnostic models in clinical applications by providing a benchmark for digital pathology, though it is incremental as it focuses on evaluation rather than new methods.
The authors tackled the problem of evaluating deep neural network robustness to image corruptions in digital pathology by establishing a benchmark with nine common corruption types, finding that models suffer significant accuracy drops (double the error on clean images) and unreliable confidence estimation.
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.