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

Journals

  1. Zellmer C, Tran T, Sridhar S. Seeing the bigger picture. Nature Reviews Microbiology 2021;19(12):745 View
  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
  3. Kim H, Maros M, Siegel F, Ganslandt T. Rapid Convolutional Neural Networks for Gram-Stained Image Classification at Inference Time on Mobile Devices: Empirical Study from Transfer Learning to Optimization. Biomedicines 2022;10(11):2808 View
  4. Herman D, Rhoads D, Schulz W, Durant T. Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review. Clinical Chemistry 2021;67(11):1466 View
  5. Weis C, Weihrauch K, Kriegsmann K, Kriegsmann M. Unsupervised Segmentation in NSCLC: How to Map the Output of Unsupervised Segmentation to Meaningful Histological Labels by Linear Combination?. Applied Sciences 2022;12(8):3718 View
  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
  7. Hindy J, Souaid T, Kovacs C. Capabilities of GPT-4o and Gemini 1.5 Pro in Gram stain and bacterial shape identification. Future Microbiology 2024;19(15):1283 View