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Deep Learning-Driven Insights into Cell Migration Dynamics

Authors:

Shruti Shrestha,

Yi Jiang,

Neranjan EdirisingheAuthors Info & Claims

PEARC '24: Practice and Experience in Advanced Research Computing 2024: Human Powered Computing

Article No.: 55, Pages 1 - 5

https://doi.org/10.1145/3626203.3670626

Published: 17 July 2024 Publication History

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Abstract

This paper presents a novel approach to discovering migrating vs. nonmigrating cells by implementing variational autoencoder architecture and training across a high-performance computing platform. The process workflow undergoes data preprocessing, training, and inferencing a deep learning architecture. The discussion covers the implementation of various hyperparameter testing throughout the study, along with the findings on training deep learning on multi-gpu vs. multi-node systems.

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Deep Learning-Driven Insights into Cell Migration Dynamics
1. Computing methodologies

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Published In

PEARC '24: Practice and Experience in Advanced Research Computing 2024: Human Powered Computing

July 2024

608 pages

ISBN:9798400704192

DOI:10.1145/3626203

Editors:
Shawn T. Brown
Hewlett-Packard Enterprise
,
Barr von Oehsen
Pittsburgh Supercomputing Center
,
Eric Adams
Purdue University
,
Eva Siegmann
Stony Brook University

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 17 July 2024

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July 21 - 25, 2024

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Overall Acceptance Rate 133 of 202 submissions, 66%

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Abstract

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Deep Learning: Methods and Applications

Deep learning algorithms for discriminant autoencoding

Applications and Challenges of Deep Learning in Computer Vision

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