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

Marc Combalia; Veronica Vilaplana

doi:10.1007/978-3-030-00889-5

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

Computer Vision and Pattern Recognition 2019-01-01 v1 Machine Learning Machine Learning

Authors: Marc Combalia , Veronica Vilaplana

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Abstract

We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial datasets and two histological datasets for breast cancer and sun exposure classification.

Keywords

histopathology image analysis medical image analysis randomized algorithm

Cite

@article{arxiv.1812.11560,
  title  = {Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification},
  author = {Marc Combalia and Veronica Vilaplana},
  journal= {arXiv preprint arXiv:1812.11560},
  year   = {2019}
}

Comments

accepted at 4th International Workshop on Deep Learning for Medical Image Analysis (DLMIA), MICCAI 2018, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer International Publishing, 2018

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

Abstract

Keywords

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