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Monte-Carlo sampling applied to multiple instance learning for whole slide image classification

Author
Combalia, M.; Vilaplana, V.
Type of activity
Presentation of work at congresses
Name of edition
First International conference on Medical Imaging with Deep Learning
Date of publication
2018
Presentation's date
2018-07-04
Book of congress proceedings
International conference on Medical Imaging with Deep Learning: Amsterdam, 4 - 6th July 2018
First page
1
Last page
3
Repository
http://hdl.handle.net/2117/126236 Open in new window
URL
https://midl.amsterdam/scientific-program/ Open in new window
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.
Citation
Combalia, M., Vilaplana, V. Monte-Carlo sampling applied to multiple instance learning for whole slide image classification. A: International conference on Medical Imaging with Deep Learning. "International conference on Medical Imaging with Deep Learning: Amsterdam, 4 - 6th July 2018". 2018, p. 1-3.
Keywords
deep learning, histological imaging, monte-carlo sampling, multiple instance learning
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

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