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- W2274520670 abstract "We describe a new approach to digital pathology that relies on measuring the optimal transportation (Kantorovich-Wasserstein) metric between pairs of nuclei obtained from histopathology images. We compare the approach to the standard feature space approach and show that our method performs at least as well if not better in automatically detecting and classifying different cancers of the liver and thyroid. 100% classification accuracy is obtained in 15 human test cases. In addition, we describe methods for using the geometric space framework to visualize and understand the differences in the data distribution that allow one to classify the data with high accuracy.KeywordsLinear Discriminant AnalysisNuclear StructureFollicular AdenomaSupport Vector Machine MethodTissue ClassThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W2274520670 date "2010-01-01" @default.
- W2274520670 modified "2023-09-26" @default.
- W2274520670 title "Detecting and Classifying Cancers from Image Data Using Optimal Transportation" @default.
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- W2274520670 doi "https://doi.org/10.1007/978-3-642-14998-6_90" @default.
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