Abstract
Cell and nuclear morphology, as observed from histopathology microscopy images, have long been known as important indicators of disease states. Due to the large amount of data, obtaining expert pathologists annotations at the individual cell level is impractical in many applications, however. Thus the majority of the approaches currently available for automated classification and cancer detection are based on utilizing the patient label for each segmented cell, and patient classification is performed by classifying single morphological exemplars (e.g. cells or subcellular features) in combination with a majority voting procedure. Here we propose a new hierarchical method for classifying sets of nuclei. The method can be interpreted as a type of multiple instance learning (MIL) method in that it embeds data from each patient into a hierarchical feature space. The feature space, and classification boundary, are alternatively optimized utilizing the support vector machine (SVM) cost function. We demonstrate the application of the method in the diagnosis of thyroid lesions and compare to existing MIL methods showing significant improvements in classification accuracy.
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This work was financially supported in part by the National Institutes of Health, grants CA 188938 and GM 090033.
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Liu, C., Huang, Y., Han, L., Ozolek, J.A., Rohde, G.K. (2016). Hierarchical Feature Extraction for Nuclear Morphometry-Based Cancer Diagnosis. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_23
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DOI: https://doi.org/10.1007/978-3-319-46976-8_23
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