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

Author
Combalia, M.; Vilaplana, V.
Type of activity
Presentation of work at congresses
Name of edition
21st International Conference on Medical Image Computing and Computer Assisted Intervention
Date of publication
2018
Presentation's date
2018-09-14
Book of congress proceedings
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018 Granada, Spain, September 20, 2018: proceedings
First page
274
Last page
281
Publisher
Springer
DOI
https://doi.org/10.1007/978-3-030-00889-5 Open in new window
Repository
http://hdl.handle.net/2117/126386 Open in new window
URL
https://www.springer.com/la/book/9783319675572 Open in new window
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.
Citation
Combalia, M., Vilaplana, V. Monte-Carlo sampling applied to multiple instance learning for histological image classification. A: International Conference on Medical Image Computing and Computer Assisted Intervention. "Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018 Granada, Spain, September 20, 2018: proceedings". Berlín: Springer, 2018, p. 274-281.
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants