题名 | Tiny Object Detector for Pulmonary Nodules based on YOLO |
作者 | |
发表日期 | 2023 |
会议名称 | 2023 7th International Conference on Deep Learning Technologies, ICDLT 2023 |
会议录名称 | ICDLT '23: Proceedings of the 2023 7th International Conference on Deep Learning Technologies
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ISBN | 9798400707520 |
页码 | 27-34 |
会议日期 | 27 July 2023 through 29 July 2023 |
会议地点 | Dalian |
会议举办国 | China |
出版地 | New York, United States |
出版者 | Association for Computing Machinery |
摘要 | Accurate detection and discovery of early lung cancer is the most effective measure to reduce lung cancer mortality with high clinical value. However, existing common object detectors show unsatisfactory detection accuracy for pulmonary nodule detection, due to the textureless appearance and small size of nodules. To address the textureless appearance problem, we propose a dedicated Nodule-Learning C3 module, which helps to extract more informative structures from limited textures of nodules. Considering that nodules' sizes are small, we further design a tiny object detection layer that performs object detection on larger feature maps, where more nodule features are preserved. Moreover, the balance between speed and accuracy is also critical for the pulmonary nodule diagnostic system. Therefore, we choose the famous one-stage detection framework YOLO [13] as our baseline and implement our proposed module and layer based on it. Extensive experimental results on the widely used benchmark LUNA16 demonstrate the superior performance of our method, in terms of both accuracy and speed. Specifically, our model improves the mAP accuracy by over and is faster than the YOLO baseline. © 2023 ACM. |
关键词 | LUNA16 Pulmonary Nodule Detection Tiny Object Detection YOLO |
DOI | 10.1145/3613330.3613337 |
URL | 查看来源 |
语种 | 英语English |
Scopus入藏号 | 2-s2.0-85175787189 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/11534 |
专题 | 理工科技学院 |
通讯作者 | Zhang, Hui |
作者单位 | 1.Department of Computer Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, China 2.Department of Computer Science, Hong Kong Baptist University, China |
第一作者单位 | 北师香港浸会大学 |
通讯作者单位 | 北师香港浸会大学 |
推荐引用方式 GB/T 7714 | Lin, Zhe,Jie, Leiping,Zhang, Hui. Tiny Object Detector for Pulmonary Nodules based on YOLO[C]. New York, United States: Association for Computing Machinery, 2023: 27-34. |
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