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Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections
The fusion relies on integrating of networks that learn region- and boundary-based representations.
Classification of Histology Sections via Multispectral Convolutional Sparse Coding
Image-based classification of histology sections plays an important role in predicting clinical outcomes.
Classification of Tumor Histology via Morphometric Context
Image-based classification of tissue histology, in terms of different components (e. g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition.
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