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

2018-12-30 · Marc Combalia, Veronica Vilaplana

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.

📄 PDF Abstract BibTeX arXiv:1812.11560

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Classification General Classification image-classification Image Classification Multiple Instance Learning

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