A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening
Abstract Accurate detection of abnormal cervical cells in cervical cancer screening increases the chances of timely treatment. The vigorous development of deep learning methods has established a new ecosystem for cervical cancer screening, which has been proven to effectively improve efficiency and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04374-5 |
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| _version_ | 1841544990967201792 |
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| author | Xuan Zhang Jianxin Ji Qi Zhang Xiaohan Zheng Kaiyuan Ge Menglei Hua Lei Cao Liuying Wang |
| author_facet | Xuan Zhang Jianxin Ji Qi Zhang Xiaohan Zheng Kaiyuan Ge Menglei Hua Lei Cao Liuying Wang |
| author_sort | Xuan Zhang |
| collection | DOAJ |
| description | Abstract Accurate detection of abnormal cervical cells in cervical cancer screening increases the chances of timely treatment. The vigorous development of deep learning methods has established a new ecosystem for cervical cancer screening, which has been proven to effectively improve efficiency and accuracy of cell detection in many studies. Although many contributing studies have been conducted, limited public datasets and time-consuming collection efforts may hinder the generalization performance of those advanced models and restrict further research. Through this work, we seek to provide a large dataset of cervical cytology images with exhaustive annotations of abnormal cervical cells. The dataset consists of 8,037 images derived from 129 scanned Thinprep cytologic test (TCT) slide images. Furthermore, we performed evaluation experiments to demonstrate the performance of representative models trained on our dataset in abnormal cells detection. |
| format | Article |
| id | doaj-art-a0d8a786804f431aa17009ac89a2ea57 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-a0d8a786804f431aa17009ac89a2ea572025-01-12T12:07:34ZengNature PortfolioScientific Data2052-44632025-01-011211810.1038/s41597-025-04374-5A large annotated cervical cytology images dataset for AI models to aid cervical cancer screeningXuan Zhang0Jianxin Ji1Qi Zhang2Xiaohan Zheng3Kaiyuan Ge4Menglei Hua5Lei Cao6Liuying Wang7Department of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Health Management, Harbin Medical UniversityAbstract Accurate detection of abnormal cervical cells in cervical cancer screening increases the chances of timely treatment. The vigorous development of deep learning methods has established a new ecosystem for cervical cancer screening, which has been proven to effectively improve efficiency and accuracy of cell detection in many studies. Although many contributing studies have been conducted, limited public datasets and time-consuming collection efforts may hinder the generalization performance of those advanced models and restrict further research. Through this work, we seek to provide a large dataset of cervical cytology images with exhaustive annotations of abnormal cervical cells. The dataset consists of 8,037 images derived from 129 scanned Thinprep cytologic test (TCT) slide images. Furthermore, we performed evaluation experiments to demonstrate the performance of representative models trained on our dataset in abnormal cells detection.https://doi.org/10.1038/s41597-025-04374-5 |
| spellingShingle | Xuan Zhang Jianxin Ji Qi Zhang Xiaohan Zheng Kaiyuan Ge Menglei Hua Lei Cao Liuying Wang A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening Scientific Data |
| title | A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening |
| title_full | A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening |
| title_fullStr | A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening |
| title_full_unstemmed | A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening |
| title_short | A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening |
| title_sort | large annotated cervical cytology images dataset for ai models to aid cervical cancer screening |
| url | https://doi.org/10.1038/s41597-025-04374-5 |
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