Authors: Piotr Keller, Muhammad Dawood, Brinder Singh Chohan, Fayyaz ul Amir Afsar Minhas
Abstract: Machine learning in computational pathology (CPath) often aggregates
patch-level predictions from multi-gigapixel Whole Slide Images (WSIs) to
generate WSI-level prediction scores for crucial tasks such as survival
prediction and drug effect prediction. However, current methods do not
explicitly characterize distributional differences between patch sets within
WSIs. We introduce HistoKernel, a novel Maximum Mean Discrepancy (MMD) kernel
that measures distributional similarity between WSIs for enhanced prediction
performance on downstream prediction tasks.
Our comprehensive analysis demonstrates HistoKernel’s effectiveness across
various machine learning tasks, including retrieval (n = 9,362), drug
sensitivity regression (n = 551), point mutation classification (n = 3,419),
and survival analysis (n = 2,291), outperforming existing deep learning
methods. Additionally, HistoKernel seamlessly integrates multi-modal data and
offers a novel perturbation-based method for patch-level explainability. This
work pioneers the use of kernel-based methods for WSI-level predictive
modeling, opening new avenues for research. Code is available at
https://github.com/pkeller00/HistoKernel.
Source: http://arxiv.org/abs/2408.05195v1
