Abstract
Autoencoding in deep learning has been known as a useful tool for extracting image features in multiple layers, which are subsequently configured for classification by deep neural networks. A practical burden for the implementation of autoencoders is the time required for training a large number of artificial neurons. This paper shows the effects of scaling of texture in the histology of colorectal cancer, which can result in significant training time reduction being approximately to an exponential function, with improved classification rates.
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Pham, T.D. (2017). Scaling of Texture in Training Autoencoders for Classification of Histological Images of Colorectal Cancer. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_61
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DOI: https://doi.org/10.1007/978-3-319-59081-3_61
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