Poster + Paper
15 March 2023 Semantic knowledge distillation for conjunctival goblet cell segmentation
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Conference Poster
Abstract
Recently, as the usage of electronic devices increase, modern people suffer from eye diseases. We analyzed goblet cells of wide-field fluorescence microscopy with a deep learning. In this study, we propose to real-time analysis using knowledge distillation using proposed loss function and optimized network. In the proposed method, residual based UNet was used as the teacher network to distill knowledge into lightweight E-Net. We train the student network using pixelwise loss and . The proposed method showed 4% improvements in dice-score compared to the lightweight E-Net, and the processing time was decreased to 68% compared to the case where only the teacher network was performed.
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Seunghyun Jang, Kyungdeok Seo, Hyunyoung Kang, Seonghan Kim, Seungyoung Kang, Ki Hean Kim, and Sejung Yang "Semantic knowledge distillation for conjunctival goblet cell segmentation", Proc. SPIE 12383, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXI, 123830I (15 March 2023); https://doi.org/10.1117/12.2648170
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KEYWORDS
Eye

Image segmentation

Deep learning

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