most citedDeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

4 citations · 4 across the 1 of their papers we have counts for

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1 paper

cs.CV2017★ 4 cited

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

Ido Cohen, Eli David, Nathan S. Netanyahu +2

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencod…