Monte-Carlo sampling applied to multiple instance learning for histological image classification
Document typeConference lecture
Rights accessRestricted access - publisher's policy
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.
CitationCombalia, 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.