ArticleJMIR research protocols2020
Deep Learning Frameworks for Rapid Gram Stain Image Data Interpretation: Protocol for a Retrospective Data Analysis.
Article in JMIR research protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- From microscopy to antimicrobial decisions: a clinically grounded roadmap for critical care infectious diseases.Frontiers in artificial intelligence · 2026Review
- Capabilities of GPT-4o and Gemini 1.5 Pro in Gram stain and bacterial shape identification.Future microbiology · 2024Article
- Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study.Biomedicines · 2023Article
- Fine-tuning Strategies for Classifying Community-Engaged Research Studies Using Transformer-Based Models: Algorithm Development and Improvement Study.JMIR formative research · 2023Article
- Rapid Convolutional Neural Networks for Gram-Stained Image Classification at Inference Time on Mobile Devices: Empirical Study from Transfer Learning to Optimization.Biomedicines · 2022Article
- Seeing the bigger picture.Nature reviews. Microbiology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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