Sample Specific Generalized Cross Entropy for Robust Histology Image Classification

Abstract

The accuracy of deep learning classifiers trained using the cross entropy loss function suffers even when a fraction of training labels are wrong or input images are uninformative. Training images and labels for computational pathology are often noisy due to the difficulty in signal localization and certain disease classifications being subjective discretization of the underlying continuums of disease conditions. For training classifiers robust to input and label noise, we propose a modified and sample-specific version of generalized cross entropy loss. We take advantage of the bootstrapping properties in deep learning models in order to design loss functions that are aware of the difficulty of classifying individual samples, due to either the label noise or the lack of a strong visual signal. We carry out extensive experiments to validate our approach by comparing against the models trained using other loss functions. The superior performance of our methodology in all the experiments support the requirement of using robust loss functions for histology image classification.