Monte-Carlo Sampling applied to Multiple Instance Learning for Whole Slide Image Classification

Marc Combalia, Verónica Vilaplana

Abstract: In this paper we propose a patch sampling strategy based on sequential Monte-Carlo methods for Whole Slide Image classification in the context of Multiple Instance Learning and show its capability to achieve high generalization performance on the differentiation between sun exposed and not sun exposed pieces of skin tissue.

Author Affiliation: Universitat Politècnica de Catalunya

Keywords: deep learning, multiple instance learning, monte carlo, sampling, whole slide images, tissue, sun exposure