组织学
分级(工程)
H&E染色
病理
卷积神经网络
基质
计算机科学
人工智能
生物
医学
染色
免疫组织化学
生态学
作者
Mina Khoshdeli,Alexander D. Borowsky,Bahram Parvin
标识
DOI:10.1109/embc.2018.8512357
摘要
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.
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