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16 March 2023 Glomerulus quantification with deep learning based on novel multi-modal label-free quantitative phase imaging from a near-infrared (Conference Presentation)
Hyewon Cho, Nurbolat Aimakov, Inwoo Park, Myeonghoon Choi, Yerim Kim, Geosong Na, Sunghoon Lim, Woonggyu Jung
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Proceedings Volume PC12389, Quantitative Phase Imaging IX; PC123890A (2023) https://doi.org/10.1117/12.2651095
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
A novel multi-modal label-free imaging system is proposed for histopathology, which provides uniformly reconstructed virtual-stained brightfield images and corresponding QPI images. The system was tested on urinal histopathology, to detect and segment glomerulus. From each modality, over 90% of IoU scores were obtained and accelerated performance was obtained through multi-modal learning. Briefly, histopathology quantification with label-free samples is a feasible method via the proposed novel system.
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Hyewon Cho, Nurbolat Aimakov, Inwoo Park, Myeonghoon Choi, Yerim Kim, Geosong Na, Sunghoon Lim, and Woonggyu Jung "Glomerulus quantification with deep learning based on novel multi-modal label-free quantitative phase imaging from a near-infrared (Conference Presentation)", Proc. SPIE PC12389, Quantitative Phase Imaging IX, PC123890A (16 March 2023); https://doi.org/10.1117/12.2651095
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KEYWORDS
Imaging systems
Phase imaging
Light sources
Near infrared
Image registration
Image segmentation
Light emitting diodes
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