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Systems Configured for Cell-Based Histopathological Learning and Prediction and Methods Thereof
Systems Configured for Cell-Based Histopathological Learning and Prediction and Methods Thereof
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机译:配置用于基于细胞的组织病理学学习和预测及其方法
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摘要
Histopathological scoring can be based on ratios of different types of cells, and in particular, cells which exhibit a particular genotypic or phenotypic characteristic, as identified by a biological assay. Automating the scoring process with an image analysis algorithm requires both correctly delineating cells, a process known as segmentation, and classifying each cell according to its morphology and reactivity to the assay. Successful classification thus depends on both successful segmentation and successful classification, resulting in the error rates of the two steps being compounded. Systems and methods of the present disclosure reduce error by performing the cell counting and classification task in a single step using a generative adversarial network (or GAN). The present disclosure similarly employs a GAN for counting cells by representing the training data as a Gaussian at the center of each cell nucleus.
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