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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publication2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728192192
DOIs
Publication statusPublished - 2020 Sep 22
Event2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 - Virtual, Waltham, United States
Duration: 2020 Sep 212020 Sep 25

Publication series

Name2020 IEEE High Performance Extreme Computing Conference, HPEC 2020

Conference

Conference2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
CountryUnited States
CityVirtual, Waltham
Period20/9/2120/9/25

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

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