  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.
