Accepted for/Published in: JMIR Research Protocols
Date Submitted: Jan 21, 2020
Date Accepted: Apr 19, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Rapid Gram-Stain Image Data Interpretation by Means of Deep Learning Frameworks: Retrospective Data Analysis
ABSTRACT
Background:
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 addressed high-performance deep learning technology in terms of computational capability.
Objective:
Accelerate an automated Gram stain image interpretation by means of deep learning framework without additional hardware resources.
Methods:
Agility of deep learning framework will be empowered by three methodologies. Fine-tuning method, integer-arithmetic-only framework, and hyperparameter tuning will be applied and evaluated.
Results:
The choice of pre-trained models and the ideal setting for multiple hyperparameters will be determined. Those results aim to provide an empirical yet reproducible guideline for those who consider a rapid deep learning solution for Gram stain image interpretation.
Conclusions:
Deep learning solution must compromise between modeling performance and computational performance. A balanced decision is essential to find a deep learning solution for Gram stain interpretation. Such a solution would ultimately improve the efficiency of routine care.
Citation
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Copyright
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