Deep Learning Frameworks for Rapid Gram Stain Image Data Interpretation: Protocol for a Retrospective Data Analysis

JMIR Research Protocols
Hee KimMaximilian Kittel

Abstract

In recent years, remarkable progress has been made in deep learning technology and successful use cases have been introduced in the medical domain. However, not many studies have considered high-performance computing to fully appreciate the capability of deep learning technology. This paper aims to design a solution to accelerate an automated Gram stain image interpretation by means of a deep learning framework without additional hardware resources. We will apply and evaluate 3 methodologies, namely fine-tuning, an integer arithmetic-only framework, and hyperparameter tuning. The choice of pretrained models and the ideal setting for layer tuning and hyperparameter tuning will be determined. These results will provide an empirical yet reproducible guideline for those who consider a rapid deep learning solution for Gram stain image interpretation. The results are planned to be announced in the first quarter of 2021. Making a balanced decision between modeling performance and computational performance is the key for a successful deep learning solution. Otherwise, highly accurate but slow deep learning solutions can add value to routine care. DERR1-10.2196/16843.

References

Mar 22, 2013·Journal of Clinical Microbiology·Paul P Bourbeau, Nathan A Ledeboer
Mar 16, 2016·IEEE Transactions on Medical Imaging·Nima Tajbakhsh Jianming Liang
Dec 1, 2017·Journal of Clinical Microbiology·Kenneth P SmithJames E Kirby

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Citations

Sep 25, 2021·Clinical Chemistry·Daniel S HermanThomas J S Durant
Oct 1, 2021·Nature Reviews. Microbiology·Caroline ZellmerSushmita Sridhar

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Software Mentioned

Nvidia
TensorBoard
Pascal
Mobilenet
Tensorflow Lite
Apache Spark
Inception
ImageNet
Tensorflow
Google Brain

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