Deep Learning-Driven Insights into Cell Migration Dynamics

被引：0

作者：

Shrestha, Shruti ^{[1
]}

Jiang, Yi ^{[1
]}

Edirisinghe, Neranjan Suranga ^{[1
]}

机构：

[1] Georgia State Univ, Atlanta, GA 30303 USA

来源：

PRACTICE AND EXPERIENCE IN ADVANCED RESEARCH COMPUTING 2024, PEARC 2024 | 2024年

基金：

美国国家科学基金会;

关键词：

Deep Learning; Computer Vision; Autoencoders; Dimensionality Reduction; Medical Imaging;

D O I：

10.1145/3626203.3670626

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：5

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