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

  • Ido Cohen
  • Eli (Omid) David
  • Nathan S. Netanyahu
  • Noa Liscovitch
  • Gal Chechik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10614)


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 autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ido Cohen
    • 1
  • Eli (Omid) David
    • 1
  • Nathan S. Netanyahu
    • 1
    • 2
  • Noa Liscovitch
    • 3
  • Gal Chechik
    • 3
  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat-GanIsrael
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA
  3. 3.Gonda Multidisiplinary Brain Research CenterBar-Ilan UniversityRamat-GanIsrael

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