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- W3202772760 abstract "Induced pluripotent stem cells (iPSCs) have broad prospects in clinical and industrial applications, and the automatic identification of iPSCs is essential to optimize the iPSCs manufacturing. However, the challenge of obtaining large amounts of labeled images has limited the use of image analysis in the field of iPSCs. In this paper, we propose Semi-Yolo, a simple yet effective semi-supervised learning (SSL) network that combines mean-teacher SSL paradigm and the Yolo object detection network for the iPSCs identification. The designed burn-in stage in Semi-Yolo makes full use of available labeled images, providing good initialization for both student and teacher model. Then in the teacher-student mutual learning stage, the gradually progressing teacher model of Semi-Yolo generates highly confident pseudo labels for unlabeled images, which provides extra training data to update the student model in a mutually-beneficial manner. Together with a Mosaic data augmentation technique to increase the diversity of our data, Semi-Yolo achieves favorable detection performance on the collected iPSCs detection dataset. Extensive experimental results on our dataset demonstrates the effectiveness of Semi-Yolo with great improvement compared to supervised baseline, which shows better detection precision and faster detection speed than the state-of-the-art SSL object detection algorithm." @default.
- W3202772760 created "2021-10-11" @default.
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- W3202772760 date "2021-01-01" @default.
- W3202772760 modified "2023-09-27" @default.
- W3202772760 title "Semi-supervised Yolo Network for Induced Pluripotent Stem Cells Detection" @default.
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- W3202772760 doi "https://doi.org/10.1007/978-3-030-87358-5_65" @default.
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