This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (671) Imaging Informatics (302) Machine Learning (2074)

Published on in Vol 9, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16843, first published .
Deep Learning Frameworks for Rapid Gram Stain Image Data Interpretation: Protocol for a Retrospective Data Analysis

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

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

Authors of this article:

Hee Kim1 Author Orcid Image ;   Thomas Ganslandt1 Author Orcid Image ;   Thomas Miethke2 Author Orcid Image ;   Michael Neumaier3 Author Orcid Image ;   Maximilian Kittel3 Author Orcid Image
Article Authors Cited by (15) Tweetations (2) Metrics

Journals

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  2. Ferrell B. Fine-tuning Strategies for Classifying Community-Engaged Research Studies Using Transformer-Based Models: Algorithm Development and Improvement Study. JMIR Formative Research 2023;7:e41137 View
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  6. Kim H, Maros M, Miethke T, Kittel M, Siegel F, Ganslandt T. Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study. Biomedicines 2023;11(5):1333 View
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  8. Lee M, Tsai Y, Huang T, Liew D, Candia I, Chang Y. Alignment-free bacterial pathogen identification using vision transformer and image augmentation techniques in high-resolution microscopy. Biomedical Signal Processing and Control 2026;112:108401 View
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Books/Policy Documents

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Conference Proceedings

  1. Sugimoto H, Hirata K. 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI). Object Detection as Gram Positive Cocci in Gram Stained Smear Images View
  2. Kawano I, Kurumida E, Terada S, Hirata K. 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI). Classifying Gram Positive Cocci and Gram Negative Bacilli in Gram Stained Smear Images View
  3. Sugimoto H, Funatsu R, Hirata K. 2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). Predicting Geckler Classification from Gram Stained Smears Images for Sputum: Image Classification versus Object Detection View
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