Weng Yuqing, Hu Qiuping, Wang Huajia, Kuang Yinglan, Zhou Yanling, Tang Yuyan, Wang Lei, Ye Xin, Lu Xing
Department of Respiratory and Critical Medicine, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Zhuhai, Guangdong, China.
Department of Respiratory and Critical Medicine, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
J Imaging Inform Med. 2024 Dec 12. doi: 10.1007/s10278-024-01348-8.
Circulating genetically abnormal cells (CACs) serve as crucial biomarkers for lung cancer diagnosis. Detecting CACs holds great value for early diagnosis and screening of lung cancer. To aid the identification of CACs, we have incorporated deep learning algorithms into our CACs detection system, specifically developing algorithms for cell segmentation and signal point detection. However, it is noteworthy that deep learning algorithms require extensive data labeling. Consequently, this study introduces a semi-supervised learning algorithm for CACs detection. For the cell segmentation task, a combination of self-training and Mean Teacher method was adopted in the semi-supervised training cell segmentation task. Furthermore, an Adaptive Mean Teacher approach was developed based on the Mean Teacher to enhance the effectiveness of semi-supervised cell segmentation. Regarding the signal point detection task, an end-to-end semi-supervised signal point detection algorithm was developed using the Adaptive Mean Teacher as the paradigm, and a Domain-Knowledge Pseudo Label was developed to improve the quality of pseudo-labeling and further enhance signal point detection. By incorporating semi-supervised training in both sub-tasks, the reliance on labeled data is reduced, thereby improving the performance of CACs detection. Our proposed semi-supervised method has achieved good results in cell segmentation tasks, signal point detection tasks, and the final CACs detection task. In the final CACs detection task, with 2%, 5%, and 10% of labeled data, our proposed semi-supervised method achieved 27.225%, 23.818%, and 4.513%, respectively. Experimental results demonstrated that the proposed method is effective.
循环基因异常细胞（CACs）是肺癌诊断的关键生物标志物。检测CACs对肺癌的早期诊断和筛查具有重要价值。为了辅助CACs的识别，我们将深度学习算法融入到CACs检测系统中，专门开发了细胞分割和信号点检测算法。然而，值得注意的是，深度学习算法需要大量的数据标注。因此，本研究引入了一种用于CACs检测的半监督学习算法。对于细胞分割任务，在半监督训练细胞分割任务中采用了自训练和均值教师方法相结合的方式。此外，基于均值教师方法开发了一种自适应均值教师方法，以提高半监督细胞分割的有效性。对于信号点检测任务，以自适应均值教师方法为范式开发了一种端到端的半监督信号点检测算法，并开发了一种领域知识伪标签来提高伪标签的质量，进一步增强信号点检测。通过在两个子任务中都采用半监督训练，减少了对标注数据的依赖，从而提高了CACs检测的性能。我们提出的半监督方法在细胞分割任务、信号点检测任务和最终的CACs检测任务中都取得了良好的结果。在最终的CACs检测任务中，使用2%、5%和10%的标注数据时，我们提出的半监督方法分别达到了27.225%、23.818%和4.513%。实验结果表明，该方法是有效的。
