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

Autor
Combalia, M.; Vilaplana, V.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
21st International Conference on Medical Image Computing and Computer Assisted Intervention
Any de l'edició
2018
Data de presentació
2018-09-14
Llibre d'actes
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
Pàgina inicial
274
Pàgina final
281
Editor
Springer
DOI
https://doi.org/10.1007/978-3-030-00889-5 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/126386 Obrir en finestra nova
URL
https://www.springer.com/la/book/9783319675572 Obrir en finestra nova
Resum
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.
Citació
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.
Grup de recerca
GPI - Grup de Processament d'Imatge i Vídeo
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants