1. Deep Learning Frameworks for Rapid Gram Stain Image Data Interpretation: Protocol for a Retrospective Data Analysis
- Author
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Kim, Hee, Ganslandt, Thomas, Miethke, Thomas, Neumaier, Michael, and Kittel, Maximilian
- Subjects
Medicine ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
BackgroundIn 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. ObjectiveThis 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. MethodsWe will apply and evaluate 3 methodologies, namely fine-tuning, an integer arithmetic–only framework, and hyperparameter tuning. ResultsThe 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. ConclusionsMaking 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. International Registered Report Identifier (IRRID)DERR1-10.2196/16843
- Published
- 2020
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