Rectified cross-entropy and upper transition loss for weakly supervised whole slide image classifier

Abstract

Convolutional neural network (CNN) has achieved promising results in classifying histopathology images so far. However, most clinical data only has label information for the whole tissue slide and annotating every region of different tissue type is prohibitively expensive. Hence, computer aided diagnosis of whole slide images (WSIs) is challenging due to: (1) a WSI contains tissues with different types but it is classified by the most malignant tissue; (2) the gigapixel size of WSIs makes loading the whole image and end-to-end CNN training computationally infeasible. Previous works tended to classify WSI patch-wisely using the whole slide label and overlooked one useful information: it is an error to classify a patch as higher-grade classes. To address this, we propose a rectified cross-entropy loss as a combination of soft pooling and hard pooling of discriminative patches. We also introduce an upper transition loss to restrain errors. Our experimental results on colon polyp WSIs showed that, the two new losses can effectively guide the CNN optimization. With only WSI class information available for training, the patch-wise classification results on the testing set largely agree with human experts’ domain knowledge.