Data

SNPGram21 - Gram stain whole slide images

The University of Queensland
Dr Sarah Abdulaziz S Alhammad (Aggregated by) Ms Sarah Abdulaziz S Alhammad (Aggregated by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/afac7f6&rft.title=SNPGram21 - Gram stain whole slide images&rft.identifier=RDM ID: f0062280-5d1e-11ee-80c8-11dcca6dca16&rft.publisher=The University of Queensland&rft.description=Dataset introduced in the published ICPR 2022 paper Efficient Cell Labelling for Gram Stain WSIs The proposed SNPGram21 dataset comprises Gram stained smears which were automatically digitised into TIF format using a 63×objective lens magnification without oil immersion. This dataset contains 100 WSIs with a resolution of 20164×11828. The WSIs are obtained from six different benches comprising: Genital, Skin, Respiratory, Fluids, Ear/Eye/Nose/Throat, and Hospital. Samples from these different groups can have variant background information. Bacteria, yeast, and dipping artifacts may present in the WSIs in addition to the cells of interest which are epithelial and leukocyte cells. Each WSI was compressed to JPEG format and then the Labelme tool was used to annotate the leukocytes and epithelial cells using bounding boxes. Please refer to the paper Efficient Cell Labelling for Gram Stain WSIs for more details.&rft.creator=Dr Sarah Abdulaziz S Alhammad&rft.creator=Ms Sarah Abdulaziz S Alhammad&rft.date=2023&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=Pathology (excl. oral pathology)&rft_subject=Clinical sciences&rft_subject=BIOMEDICAL AND CLINICAL SCIENCES&rft_subject=Computer vision&rft_subject=Computer vision and multimedia computation&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=dataset&rft.language=English Access the data
Cite Saved to MyRDA Save to MyRDA

Contact Information

[email protected]
School of Electrical Engineering and Computer Science

Full description

Dataset introduced in the published ICPR 2022 paper "Efficient Cell Labelling for Gram Stain WSIs" The proposed SNPGram21 dataset comprises Gram stained smears which were automatically digitised into TIF format using a 63×objective lens magnification without oil immersion. This dataset contains 100 WSIs with a resolution of 20164×11828. The WSIs are obtained from six different benches comprising: Genital, Skin, Respiratory, Fluids, Ear/Eye/Nose/Throat, and Hospital. Samples from these different groups can have variant background information. Bacteria, yeast, and dipping artifacts may present in the WSIs in addition to the cells of interest which are epithelial and leukocyte cells. Each WSI was compressed to JPEG format and then the Labelme tool was used to annotate the leukocytes and epithelial cells using bounding boxes. Please refer to the paper "Efficient Cell Labelling for Gram Stain WSIs" for more details.

Issued: 29 09 2023

This dataset is part of a larger collection

Click to explore relationships graph
Help

Related People

Related Grants and Projects

Related Websites

Subjects
Biomedical and Clinical Sciences | Clinical Sciences | Computer Vision | Computer Vision and Multimedia Computation | Information and Computing Sciences | Pathology (Excl. Oral Pathology) | eng |

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Other Information
Efficient cell labelling for gram stain WSIs

local : UQ:4591992

Alhammad, Sarah, Zhang, Teng, Zhao, Kun, Hobson, Peter, Jennings, Anthony and Lovell, Brian C. (2022). Efficient cell labelling for gram stain WSIs. 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 21-25 August 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/icpr56361.2022.9956490

Research Data Collections

local : UQ:289097

Identifiers