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

doi:10.1007/978-3-319-68612-7_33

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

Bar-Ilan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
Editors	Alessandra Lintas, Alessandro E. Villa, Stefano Rovetta, Paul F. Verschure
Publisher	Springer Verlag
Pages	287-296
Number of pages	10
ISBN (Print)	9783319686110
DOIs	https://doi.org/10.1007/978-3-319-68612-7_33
State	Published - 2017
Event	26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy Duration: 11 Sep 2017 → 14 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10614 LNCS

Conference

Conference	26th International Conference on Artificial Neural Networks, ICANN 2017
Country/Territory	Italy
City	Alghero
Period	11/09/17 → 14/09/17

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-68612-7_33

Cite this

Cohen, I., David, E. O., Netanyahu, N. S., Liscovitch, N., & Chechik, G. (2017). DeepBrain: Functional representation of neural in-situ hybridization images for gene ontology classification using deep convolutional autoencoders. In A. Lintas, A. E. Villa, S. Rovetta, & P. F. Verschure (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings (pp. 287-296). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10614 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68612-7_33

Cohen, Ido ; David, Eli Omid ; Netanyahu, Nathan S. et al. / DeepBrain : Functional representation of neural in-situ hybridization images for gene ontology classification using deep convolutional autoencoders. Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings. editor / Alessandra Lintas ; Alessandro E. Villa ; Stefano Rovetta ; Paul F. Verschure. Springer Verlag, 2017. pp. 287-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "DeepBrain: Functional representation of neural in-situ hybridization images for gene ontology classification using deep convolutional autoencoders",

abstract = "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.",

author = "Ido Cohen and David, {Eli Omid} and Netanyahu, {Nathan S.} and Noa Liscovitch and Gal Chechik",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 26th International Conference on Artificial Neural Networks, ICANN 2017 ; Conference date: 11-09-2017 Through 14-09-2017",

year = "2017",

doi = "10.1007/978-3-319-68612-7_33",

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isbn = "9783319686110",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "Alessandra Lintas and Villa, {Alessandro E.} and Stefano Rovetta and Verschure, {Paul F.}",

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Cohen, I, David, EO, Netanyahu, NS, Liscovitch, N & Chechik, G 2017, DeepBrain: Functional representation of neural in-situ hybridization images for gene ontology classification using deep convolutional autoencoders. in A Lintas, AE Villa, S Rovetta & PF Verschure (eds), Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10614 LNCS, Springer Verlag, pp. 287-296, 26th International Conference on Artificial Neural Networks, ICANN 2017, Alghero, Italy, 11/09/17. https://doi.org/10.1007/978-3-319-68612-7_33

DeepBrain: Functional representation of neural in-situ hybridization images for gene ontology classification using deep convolutional autoencoders. / Cohen, Ido; David, Eli Omid; Netanyahu, Nathan S. et al.
Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings. ed. / Alessandra Lintas; Alessandro E. Villa; Stefano Rovetta; Paul F. Verschure. Springer Verlag, 2017. p. 287-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10614 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - DeepBrain

T2 - 26th International Conference on Artificial Neural Networks, ICANN 2017

AU - Cohen, Ido

AU - David, Eli Omid

AU - Netanyahu, Nathan S.

AU - Liscovitch, Noa

AU - Chechik, Gal

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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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M3 - منشور من مؤتمر

SN - 9783319686110

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 287

EP - 296

BT - Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings

A2 - Lintas, Alessandra

A2 - Villa, Alessandro E.

A2 - Rovetta, Stefano

A2 - Verschure, Paul F.

PB - Springer Verlag

Y2 - 11 September 2017 through 14 September 2017

ER -

Cohen I, David EO, Netanyahu NS, Liscovitch N, Chechik G. DeepBrain: Functional representation of neural in-situ hybridization images for gene ontology classification using deep convolutional autoencoders. In Lintas A, Villa AE, Rovetta S, Verschure PF, editors, Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings. Springer Verlag. 2017. p. 287-296. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-68612-7_33

DeepBrain: Functional representation of neural in-situ hybridization images for gene ontology classification using deep convolutional autoencoders

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