Abstract
The number of signals in each signal channel in the nucleus of a four-color FISH image is the basis for distinguishing between normal cells, deletion signal cells, and CACs. In previous studies, we adopted deep learning for signal detection, which required a relatively large number of voxel-level labeled cells and signal images for training. In this study, we introduce a mean teacher mechanism into the training process and propose an end-to-end semi-supervised object detection method to detect the signal. We also propose Domain-Adaptive Pseudo Labels as a false positive filtering based on the prior knowledge of CAC signal. The experimental results show that the strategies proposed is simple and effective. On the four-color FISH image, when using only 8% of labeled data is used, it can all achieve 0.15% 0.41% 0.55% and 0.85% F1 score improvements compared to the supervised baseline.
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Wang, H., Kuang, Y., Fan, X., Zhou, Y., Ye, X., Lu, X. (2023). Domain Knowledge Adapted Semi-supervised Learning with Mean-Teacher Strategy for Circulating Abnormal Cells Identification. In: Qin, W., Zaki, N., Zhang, F., Wu, J., Yang, F., Li, C. (eds) Computational Mathematics Modeling in Cancer Analysis. CMMCA 2023. Lecture Notes in Computer Science, vol 14243. Springer, Cham. https://doi.org/10.1007/978-3-031-45087-7_7
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