Class-balanced Open-set Semi-supervised Object Detection for Medical Images
<|reference_start|>Class-balanced Open-set Semi-supervised Object Detection for Medical Images: 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.<|reference_end|>
@article{lu2024class-balanced,
    title={Class-balanced Open-set Semi-supervised Object Detection for Medical
  Images},
    author={Zhanyun Lu, Renshu Gu, Huimin Cheng, Siyu Pang, Mingyu Xu, Peifang Xu,
  Yaqi Wang, Yuichiro Kinoshita, Juan Ye, Gangyong Jia, Qing Wu},
    journal={arXiv preprint arXiv:2408.12355},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12355},
    primaryClass={cs.CV cs.AI}
}