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PREDICTING OVERALL SURVIVAL IN EARLY STAGE LUNG CANCER WITH FEATURE DRIVEN LOCAL CELL GRAPHS (FEDEG)
PREDICTING OVERALL SURVIVAL IN EARLY STAGE LUNG CANCER WITH FEATURE DRIVEN LOCAL CELL GRAPHS (FEDEG)
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机译:使用功能驱动的局部细胞图(FEDEG)预测早期肺癌的整体生存
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摘要
Embodiments include accessing an image of a region of tissue demonstrating cancerous pathology; detecting a plurality of cells represented in the image; segmenting a cellular nucleus of a first member of the plurality of cells and a cellular nucleus of at least one second, different member of the plurality of cells; extracting a set of nuclear morphology features from the plurality of cells; constructing a feature driven local cell graph (FeDeG) based on the set of nuclear morphology features and a spatial relationship between the cellular nuclei using a mean-shift clustering approach; computing a set of FeDeG features based on the FeDeG; providing the FeDeG features to a machine learning classifier; receiving, from the machine learning classifier, a classification of the region of tissue as a long-term or a short-term survivor, based, at least in part, on the set of FeDeG features; and displaying the classification.
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