Class-balanced Open-set Semi-supervised Object Detection for Medical Images
- URL: http://arxiv.org/abs/2408.12355v1
- Date: Thu, 22 Aug 2024 12:54:15 GMT
- Title: Class-balanced Open-set Semi-supervised Object Detection for Medical Images
- Authors: Zhanyun Lu, Renshu Gu, Huimin Cheng, Siyu Pang, Mingyu Xu, Peifang Xu, Yaqi Wang, Yuichiro Kinoshita, Juan Ye, Gangyong Jia, Qing Wu,
- Abstract summary: 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.
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.
Our method outperforms the state-of-the-art SSOD performance, achieving a 4.25 mAP improvement on the public dataset.
- Score: 11.376169783120213
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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