A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection
Peripheral blood cell detection is essential for diagnosing and monitoring hematologic disorders. However, existing methods typically focus on a limited number of cell types (usually 3 to 10), restricting their ability to detect a broader range of cells. These methods also struggle with class imbala...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11036778/ |
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| author | Yuheng Feng Jiangtao He Linjin Wang Wuchen Yang Sihan Deng Lanlin Li Xinwei Li |
| author_facet | Yuheng Feng Jiangtao He Linjin Wang Wuchen Yang Sihan Deng Lanlin Li Xinwei Li |
| author_sort | Yuheng Feng |
| collection | DOAJ |
| description | Peripheral blood cell detection is essential for diagnosing and monitoring hematologic disorders. However, existing methods typically focus on a limited number of cell types (usually 3 to 10), restricting their ability to detect a broader range of cells. These methods also struggle with class imbalance and still rely heavily on large amounts of annotated data, limiting their effectiveness for underrepresented categories and hindering scalability. To address these challenges, we propose a novel composite multi-strategy active learning framework using YOLOv5 for enhanced peripheral blood cell detection. The framework reduces annotation costs and improves detection performance by combining uncertainty-based selection, diversity querying, and density-based querying to prioritize the most informative and diverse samples. The process begins with entropy-based uncertainty selection to identify the most uncertain samples, followed by clustering analysis to capture diverse samples from the feature space, and concludes with density-based selection using the k-nearest neighbors algorithm to prioritize samples from high-density regions. Experimental results demonstrate that the framework achieves a mean average precision (mAP@0.5) of 64.8% on a private dataset with 26 cell types, outperforming other active learning strategies and existing methods. It also reduces manual annotation workload by 28.7% compared to random sampling. On the public BCCD dataset (3 cell types), the framework achieves an mAP@0.5 of 86.7%. These results highlight the practicality and reliability of the proposed framework for optimizing peripheral blood cell detection. Our code can be accessed at: <uri>https://github.com/Sar-fyh/bme</uri> |
| format | Article |
| id | doaj-art-c762eca0dc054afeaa2cba0f9c60d0b8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-c762eca0dc054afeaa2cba0f9c60d0b82025-08-20T03:16:18ZengIEEEIEEE Access2169-35362025-01-011310481510482710.1109/ACCESS.2025.357991811036778A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image DetectionYuheng Feng0https://orcid.org/0009-0009-2315-7362Jiangtao He1https://orcid.org/0009-0002-7763-491XLinjin Wang2https://orcid.org/0000-0003-1951-5680Wuchen Yang3Sihan Deng4Lanlin Li5https://orcid.org/0009-0006-3616-3962Xinwei Li6https://orcid.org/0000-0003-0713-9366School of Life Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Life Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Life Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaMedical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, ChinaSchool of Media Arts, Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Defang Information Technology Company Ltd., Chongqing, ChinaSchool of Life Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaPeripheral blood cell detection is essential for diagnosing and monitoring hematologic disorders. However, existing methods typically focus on a limited number of cell types (usually 3 to 10), restricting their ability to detect a broader range of cells. These methods also struggle with class imbalance and still rely heavily on large amounts of annotated data, limiting their effectiveness for underrepresented categories and hindering scalability. To address these challenges, we propose a novel composite multi-strategy active learning framework using YOLOv5 for enhanced peripheral blood cell detection. The framework reduces annotation costs and improves detection performance by combining uncertainty-based selection, diversity querying, and density-based querying to prioritize the most informative and diverse samples. The process begins with entropy-based uncertainty selection to identify the most uncertain samples, followed by clustering analysis to capture diverse samples from the feature space, and concludes with density-based selection using the k-nearest neighbors algorithm to prioritize samples from high-density regions. Experimental results demonstrate that the framework achieves a mean average precision (mAP@0.5) of 64.8% on a private dataset with 26 cell types, outperforming other active learning strategies and existing methods. It also reduces manual annotation workload by 28.7% compared to random sampling. On the public BCCD dataset (3 cell types), the framework achieves an mAP@0.5 of 86.7%. These results highlight the practicality and reliability of the proposed framework for optimizing peripheral blood cell detection. Our code can be accessed at: <uri>https://github.com/Sar-fyh/bme</uri>https://ieeexplore.ieee.org/document/11036778/Peripheral blood cellsactive learningobject detectiondiversity query |
| spellingShingle | Yuheng Feng Jiangtao He Linjin Wang Wuchen Yang Sihan Deng Lanlin Li Xinwei Li A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection IEEE Access Peripheral blood cells active learning object detection diversity query |
| title | A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection |
| title_full | A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection |
| title_fullStr | A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection |
| title_full_unstemmed | A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection |
| title_short | A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection |
| title_sort | multi strategy active learning framework for enhanced peripheral blood cell image detection |
| topic | Peripheral blood cells active learning object detection diversity query |
| url | https://ieeexplore.ieee.org/document/11036778/ |
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