Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide Images
This work addresses the challenge of WSI similarity for predictive and prognostic modeling in computational pathology, offering a novel method with specific gains.
The authors tackled the problem of measuring similarity between whole slide images (WSIs) in computational pathology, proposing a kernelized Maximum Mean Discrepancy (MMD) method that achieved state-of-the-art performance in predicting TP-53 mutation status and survival analysis.
How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determination of degree of similarity between two WSIs is a challenging task with a number of practical applications. In this work, we explore a novel strategy based on kernelized Maximum Mean Discrepancy (MMD) analysis for determination of pairwise similarity between WSIs. The proposed approach works by calculating MMD between two WSIs using kernels over deep features of image patches. This allows representation of an entire dataset of WSIs as a kernel matrix for WSI level clustering, weakly-supervised prediction of TP-53 mutation status in breast cancer patients from their routine WSIs as well as survival analysis with state of the art prediction performance. We believe that this work will open up further avenues for application of WSI-level kernels for predictive and prognostic tasks in computational pathology.