Abstract
Orderly automatic collection of brain slices on the silicon substrate is critical for understanding the working principle of the whole-brain neural network. Accurate and real-time brain slices detection with microscopic CCD is crucial for automatic collection of brain slices. To solve this task, an efficient simplified SSD detection model with Cycle-GAN data augmentation is presented in this paper. The proposed simplified SSD streamlines the detection network of the original SSD architecture, leading to a more rapid detection. Moreover, the proposed Cycle-GAN data augmentation method overcomes the limitation of training images. To verify the effectiveness of the proposed method, experiments are conducted with a self-made brain slices dataset. The experiment results suggest that, the proposed method has a good performance of rapidly detecting brain slices with only a small training dataset.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61873268, 61633016, in part by the Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program, in part by the Beijing Municipal Natural Science Foundation under Grant 4162066.
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Liu, W., Cheng, L., Meng, D. (2018). Brain Slices Microscopic Detection Using Simplified SSD with Cycle-GAN Data Augmentation. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_40
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DOI: https://doi.org/10.1007/978-3-030-04212-7_40
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