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

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Adam Michaleas , Lars Gjesteby , M 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 ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1109/hpec43674.2020.9286225

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 100$\times$ 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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Computer Science

Xeon

Benchmark (Surveying)

Artificial Intelligence

Concepts

Pipeline (software) Computer science Scalability Xeon Throughput Benchmark (surveying) Artificial intelligence Image processing Computer architecture Machine learning Layer (electronics) Xeon Phi Parallel computing Embedded system Image (mathematics) Operating system Organic chemistry Geodesy Wireless Chemistry Geography

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1109/hpec43674.2020.9286225
OA Status: green
Cited By: 1
References: 34
Related Works: 20
OpenAlex ID: https://openalex.org/W3038076319

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OpenAlex ID: https://openalex.org/W3038076319

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DOI: https://doi.org/10.1109/hpec43674.2020.9286225

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

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-09-22

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Authors: Adam Michaleas, Lars Gjesteby, M 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

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Landing page: https://doi.org/10.1109/hpec43674.2020.9286225

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2006.14684

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Concepts: Pipeline (software), Computer science, Scalability, Xeon, Throughput, Benchmark (surveying), Artificial intelligence, Image processing, Computer architecture, Machine learning, Layer (electronics), Xeon Phi, Parallel computing, Embedded system, Image (mathematics), Operating system, Organic chemistry, Geodesy, Wireless, Chemistry, Geography

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Cited by: 1

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Citations by year (recent): 2021: 1

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References (count): 34

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