ArXiv Preprint
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.
Piotr Keller, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas
2023-01-23
