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- W4316171110 abstract "Abstract In this paper, we use deep learning techniques to segment different regions from breast cancer histopathology images, such as tumor nucleus, epithelium and stromal areas. Then, in the second stage, the deep segmentation features learned by the neural network are used to predict individual patient survival, using random forest based classification. We show that the deep segmentation network features can predict survival very well, and outperform classical computer vision based shape, texture and other feature descriptors used in earlier research for the same survival prediction task." @default.
- W4316171110 created "2023-01-15" @default.
- W4316171110 creator A5024044326 @default.
- W4316171110 date "2023-01-14" @default.
- W4316171110 modified "2023-09-30" @default.
- W4316171110 title "Histopathology: Deep machine learning based semantic segmentation features predict patient survival" @default.
- W4316171110 cites W267005183 @default.
- W4316171110 doi "https://doi.org/10.1101/2023.01.14.23284554" @default.
- W4316171110 hasPublicationYear "2023" @default.
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