BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation
Abstract In the past several years, a few cervical Pap smear datasets have been published for use in clinical training. However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conv...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04328-3 |
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| author | E. Celeste Welch Chenhao Lu C. James Sung Cunxian Zhang Anubhav Tripathi Joyce Ou |
| author_facet | E. Celeste Welch Chenhao Lu C. James Sung Cunxian Zhang Anubhav Tripathi Joyce Ou |
| author_sort | E. Celeste Welch |
| collection | DOAJ |
| description | Abstract In the past several years, a few cervical Pap smear datasets have been published for use in clinical training. However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conventional Pap smear method. Multicellular liquid Pap image datasets are a more accurate reflection of current cervical screening techniques. While a multicellular liquid SurePath™ dataset has been created, machine learning models struggle to classify a test image set when it is prepared differently from the training set due to visual differences. Therefore, this dataset of multicellular Pap smear images prepared with the more common ThinPrep® protocol is presented as a helpful resource for training and testing artificial intelligence models, particularly for future application in cervical dysplasia diagnosis. The “Brown Multicellular ThinPrep” (BMT) dataset is the first publicly available multicellular ThinPrep® dataset, consisting of 600 clinically vetted images collected from 180 Pap smear slides from 180 patients, classified into three key diagnostic categories. |
| format | Article |
| id | doaj-art-6752cd08518f40d68dfcadd532a1da21 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-6752cd08518f40d68dfcadd532a1da212024-12-29T12:10:04ZengNature PortfolioScientific Data2052-44632024-12-011111810.1038/s41597-024-04328-3BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and ValidationE. Celeste Welch0Chenhao Lu1C. James Sung2Cunxian Zhang3Anubhav Tripathi4Joyce Ou5Center for Biomedical Engineering, School of Engineering, Brown UniversityDepartment of Computer Science, Brown UniversityDepartment of Pathology and Laboratory Medicine, Alpert Medical School, Brown UniversityDepartment of Pathology and Laboratory Medicine, Alpert Medical School, Brown UniversityCenter for Biomedical Engineering, School of Engineering, Brown UniversityDepartment of Pathology and Laboratory Medicine, Alpert Medical School, Brown UniversityAbstract In the past several years, a few cervical Pap smear datasets have been published for use in clinical training. However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conventional Pap smear method. Multicellular liquid Pap image datasets are a more accurate reflection of current cervical screening techniques. While a multicellular liquid SurePath™ dataset has been created, machine learning models struggle to classify a test image set when it is prepared differently from the training set due to visual differences. Therefore, this dataset of multicellular Pap smear images prepared with the more common ThinPrep® protocol is presented as a helpful resource for training and testing artificial intelligence models, particularly for future application in cervical dysplasia diagnosis. The “Brown Multicellular ThinPrep” (BMT) dataset is the first publicly available multicellular ThinPrep® dataset, consisting of 600 clinically vetted images collected from 180 Pap smear slides from 180 patients, classified into three key diagnostic categories.https://doi.org/10.1038/s41597-024-04328-3 |
| spellingShingle | E. Celeste Welch Chenhao Lu C. James Sung Cunxian Zhang Anubhav Tripathi Joyce Ou BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation Scientific Data |
| title | BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation |
| title_full | BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation |
| title_fullStr | BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation |
| title_full_unstemmed | BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation |
| title_short | BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation |
| title_sort | bmt a cross validated thinprep pap cervical cytology dataset for machine learning model training and validation |
| url | https://doi.org/10.1038/s41597-024-04328-3 |
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