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- W2899389586 abstract "Aberration in tissue architecture is an essential index for cancer diagnosis and tumor grading. Therefore, extracting features of aberrant phenotypes and classification of the histology tissue can provide a model for computer-aided pathology (CAP). As a case study, we investigate the application of convolutional neural networks (CNN)s for tumor grading and decomposing tumor architecture from hematoxylin and eosin (H&E) stained histology sections of kidney. The former and latter contribute to CAP and the role of the tumor architecture on the outcome (e.g., survival), respectively. A training set is constructed and sample images are classified into six categories of normal, fat, blood, stroma, low-grade granular tumor, and high-grade clear cell carcinoma. We have compared the performances of a deep versus shallow networks, and shown that the deeper model outperforms the shallow network." @default.
- W2899389586 created "2018-11-09" @default.
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- W2899389586 date "2018-07-01" @default.
- W2899389586 modified "2023-09-24" @default.
- W2899389586 title "Deep Learning Models Differentiate Tumor Grades from H&E Stained Histology Sections" @default.
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- W2899389586 doi "https://doi.org/10.1109/embc.2018.8512357" @default.
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