Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

Adam Michaleas; Lars A. Gjesteby; Michael Snyder; David Chavez; Meagan Ash; Matthew A. Melton; Damon G. Lamb; Sara N. Burke; Kevin J. Otto; Lee Kamentsky; Webster Guan; Kwanghun Chung; Laura J. Brattain

doi:10.1109/HPEC43674.2020.9286225

Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

Adam Michaleas, Lars A. Gjesteby, Michael Snyder, David Chavez, Meagan Ash, Matthew A. Melton, Damon G. Lamb, Sara N. Burke, Kevin J. Otto, Lee Kamentsky, Webster Guan, Kwanghun Chung, Laura J. Brattain

Yonsei Advanced Science Institute

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

3 Citations (Scopus)

Abstract

This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100x increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.

Original language	English
Title of host publication	2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728192192
DOIs	https://doi.org/10.1109/HPEC43674.2020.9286225
Publication status	Published - 2020 Sept 22
Event	2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 - Virtual, Waltham, United States Duration: 2020 Sept 21 → 2020 Sept 25

Publication series

Name	2020 IEEE High Performance Extreme Computing Conference, HPEC 2020

Conference

Conference	2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Country/Territory	United States
City	Virtual, Waltham
Period	20/9/21 → 20/9/25

Bibliographical note

Funding Information:
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the Defense Advanced Research Projects Agency under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency. 2020 Massachusetts Institute of Technology. Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.This work was also sponsored by the National Institutes of Health NIH 1U01MH117072. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Institutes of Health. The work performed by University of Florida was sponsored by the Defense Advanced Research Projects Agency (DARPA) BTO under the auspices of Dr. Douglas Weber and Dr. Tristan McClure-Begley through the DARPA Contracts Management Office Grant No. HR0011-17-2-0019.

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Access to Document

10.1109/HPEC43674.2020.9286225

Cite this

Michaleas, A., Gjesteby, L. A., Snyder, M., Chavez, D., Ash, M., Melton, M. A., Lamb, D. G., Burke, S. N., Otto, K. J., Kamentsky, L., Guan, W., Chung, K., & Brattain, L. J. (2020). Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment. In 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 [9286225] (2020 IEEE High Performance Extreme Computing Conference, HPEC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPEC43674.2020.9286225

@inproceedings{f2596daba92c43b7a935de495f0c16bd,

title = "Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment",

abstract = "This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100x increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.",

author = "Adam Michaleas and Gjesteby, {Lars A.} and Michael Snyder and David Chavez and Meagan Ash and Melton, {Matthew A.} and Lamb, {Damon G.} and Burke, {Sara N.} and Otto, {Kevin J.} and Lee Kamentsky and Webster Guan and Kwanghun Chung and Brattain, {Laura J.}",

note = "Funding Information: DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the Defense Advanced Research Projects Agency under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency. 2020 Massachusetts Institute of Technology. Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.This work was also sponsored by the National Institutes of Health NIH 1U01MH117072. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Institutes of Health. The work performed by University of Florida was sponsored by the Defense Advanced Research Projects Agency (DARPA) BTO under the auspices of Dr. Douglas Weber and Dr. Tristan McClure-Begley through the DARPA Contracts Management Office Grant No. HR0011-17-2-0019. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 ; Conference date: 21-09-2020 Through 25-09-2020",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Michaleas, A, Gjesteby, LA, Snyder, M, Chavez, D, Ash, M, Melton, MA, Lamb, DG, Burke, SN, Otto, KJ, Kamentsky, L, Guan, W, Chung, K & Brattain, LJ 2020, Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment. in 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020., 9286225, 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020, Virtual, Waltham, United States, 20/9/21. https://doi.org/10.1109/HPEC43674.2020.9286225

Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment. / Michaleas, Adam; Gjesteby, Lars A.; Snyder, Michael et al.
2020 IEEE High Performance Extreme Computing Conference, HPEC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9286225 (2020 IEEE High Performance Extreme Computing Conference, HPEC 2020).

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

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AU - Chavez, David

AU - Ash, Meagan

AU - Melton, Matthew A.

AU - Lamb, Damon G.

AU - Burke, Sara N.

AU - Otto, Kevin J.

AU - Kamentsky, Lee

AU - Guan, Webster

AU - Chung, Kwanghun

AU - Brattain, Laura J.

N1 - Funding Information: DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the Defense Advanced Research Projects Agency under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency. 2020 Massachusetts Institute of Technology. Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.This work was also sponsored by the National Institutes of Health NIH 1U01MH117072. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Institutes of Health. The work performed by University of Florida was sponsored by the Defense Advanced Research Projects Agency (DARPA) BTO under the auspices of Dr. Douglas Weber and Dr. Tristan McClure-Begley through the DARPA Contracts Management Office Grant No. HR0011-17-2-0019. Publisher Copyright: © 2020 IEEE.

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N2 - This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100x increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.

AB - This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100x increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.

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M3 - Conference contribution

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BT - 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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Michaleas A, Gjesteby LA, Snyder M, Chavez D, Ash M, Melton MA et al. Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment. In 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9286225. (2020 IEEE High Performance Extreme Computing Conference, HPEC 2020). doi: 10.1109/HPEC43674.2020.9286225

Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

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