Class-balanced Open-set Semi-supervised Object Detection for Medical Images
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913476757159936 |
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| author | Lu, Zhanyun Gu, Renshu Cheng, Huimin Pang, Siyu Xu, Mingyu Xu, Peifang Wang, Yaqi Kinoshita, Yuichiro Ye, Juan Jia, Gangyong Wu, Qing |
| author_facet | Lu, Zhanyun Gu, Renshu Cheng, Huimin Pang, Siyu Xu, Mingyu Xu, Peifang Wang, Yaqi Kinoshita, Yuichiro Ye, Juan Jia, Gangyong Wu, Qing |
| contents | Medical image datasets in the real world are often unlabeled and imbalanced, and Semi-Supervised Object Detection (SSOD) can utilize unlabeled data to improve an object detector. However, existing approaches predominantly assumed that the unlabeled data and test data do not contain out-of-distribution (OOD) classes. The few open-set semi-supervised object detection methods have two weaknesses: first, the class imbalance is not considered; second, the OOD instances are distinguished and simply discarded during pseudo-labeling. In this paper, we consider the open-set semi-supervised object detection problem which leverages unlabeled data that contain OOD classes to improve object detection for medical images. Our study incorporates two key innovations: Category Control Embed (CCE) and out-of-distribution Detection Fusion Classifier (OODFC). CCE is designed to tackle dataset imbalance by constructing a Foreground information Library, while OODFC tackles open-set challenges by integrating the ``unknown'' information into basic pseudo-labels. Our method outperforms the state-of-the-art SSOD performance, achieving a 4.25 mAP improvement on the public Parasite dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12355 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Class-balanced Open-set Semi-supervised Object Detection for Medical Images Lu, Zhanyun Gu, Renshu Cheng, Huimin Pang, Siyu Xu, Mingyu Xu, Peifang Wang, Yaqi Kinoshita, Yuichiro Ye, Juan Jia, Gangyong Wu, Qing Computer Vision and Pattern Recognition Artificial Intelligence Medical image datasets in the real world are often unlabeled and imbalanced, and Semi-Supervised Object Detection (SSOD) can utilize unlabeled data to improve an object detector. However, existing approaches predominantly assumed that the unlabeled data and test data do not contain out-of-distribution (OOD) classes. The few open-set semi-supervised object detection methods have two weaknesses: first, the class imbalance is not considered; second, the OOD instances are distinguished and simply discarded during pseudo-labeling. In this paper, we consider the open-set semi-supervised object detection problem which leverages unlabeled data that contain OOD classes to improve object detection for medical images. Our study incorporates two key innovations: Category Control Embed (CCE) and out-of-distribution Detection Fusion Classifier (OODFC). CCE is designed to tackle dataset imbalance by constructing a Foreground information Library, while OODFC tackles open-set challenges by integrating the ``unknown'' information into basic pseudo-labels. Our method outperforms the state-of-the-art SSOD performance, achieving a 4.25 mAP improvement on the public Parasite dataset. |
| title | Class-balanced Open-set Semi-supervised Object Detection for Medical Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2408.12355 |