ArticleScientific data2024
A Clinical Bacterial Dataset for Deep Learning in Microbiological Rapid On-Site Evaluation.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Overcoming resolution constraints in automated colony counting via a high-performance deep learning framework using SAHI.Scientific reports · 2026Article
- Application of few-shot learning and transfer learning based on YOLOv6 in the recognition of bacteria in sputum M-ROSE.Frontiers in cellular and infection microbiology · 2026Article
- PW-BALFC, a clinical dataset for detection and instance segmentation of bronchoalveolar lavage fluid cell.Scientific data · 2025Article
- Automation of Multi-Class Microscopy Image Classification Based on the Microorganisms Taxonomic Features Extraction.Journal of imaging · 2025Article
- Inner Ear Multiple Primary Cell Type Detection System.Scientific data · 2025Article
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
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Authors and funding
12 authors.
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Abstract
Microbiological Rapid On-Site Evaluation (M-ROSE) is based on smear staining and microscopic observation, providing critical references for the diagnosis and treatment of pulmonary infectious disease. Automatic identification of pathogens is the key to improving the quality and speed of M-ROSE. Recent advancements in deep learning have yielded numerous identification algorithms and datasets. However, most studies focus on artificially cultured bacteria and lack clinical data and algorithms. Therefore, we collected Gram-stained bacteria images from lower respiratory tract specimens of patients with lung infections in Chinese PLA General Hospital obtained by M-ROSE from 2018 to 2022 and desensitized images to produce 1705 images (4,912 × 3,684 pixels). A total of 4,833 cocci and 6,991 bacilli were manually labelled and differentiated into negative and positive. In addition, we applied the detection and segmentation networks for benchmark testing. Data and benchmark algorithms we provided that may benefit the study of automated bacterial identification in clinical specimens.
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