Classification of whole slide images for the presence of maternal vascular malperfusion lesions using attention-based, weakly supervised deep learning

Placental lesions indicative of maternal vascular malperfusion (MVM) are associated with future cardiovascular disease (CVD) in women with placenta-mediated diseases of pregnancy. Early diagnosis of CVD can reduce morbidity, mortality, and healthcare costs. MVM lesions can be detected in placental histopathology images, providing a means for CVD risk screening postnatally. Deep learning approaches, such as convolutional neural networks (CNNs), have demonstrated high potential for automating histopathology image analysis. Given the large size of histopathology whole slide images (WSIs), a patch-based approach is often employed; however, labeling is typically only available at the WSI level. MVM lesions can present heterogeneously across the image, so assigning WSI labels to patches results in patch mislabeling. In this study, we propose a weakly supervised learning method for MVM lesion classification. Features were computed from the patches extracted from WSIs using the CNN-based Resnet18 pre-trained model. An attention-based network, using weakly supervised learning, has been developed to classify WSIs as MVM+/-. The model performance was assessed against a baseline model, which was a patch-based fully supervised model. The weakly supervised learning method had an accuracy of 87.4%, which was superior to the baseline model accuracy of 81.1%. This is a promising result that suggests that weakly supervised learning can help overcome patch labeling errors that arise from the heterogeneous presentation of histopathology features, such as MVM lesions, and only having WSI-level labels.