Showing results 1 to 7 of 7
Artificial intelligence-enabled quantitative phase imaging methods for life sciences Park, Juyeon; Bai, Bijie; Ryu, Donghun; Liu, Tairan; Lee, Chungha; Luo, Yi; Lee, Mahn Jae; et al, NATURE METHODS, v.20, no.11, pp.1645 - 1660, 2023-11 |
Automated Identification of Bacteria Using Three-dimensional Holographic Imaging and Convolutional Neural Network Kim, Geon; Jo, YoungJu; Cho, Hyungjoo; Choi, Gunho; Kim, Beom-Soo; Min, Hyun-seok; Park, YongKeun, 31st Annual IEEE Photonics Conference (IPC) of the IEEE-Photonics-Society, IEEE, 2018-10 |
Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography![]() Choi, Gunho; Ryu, DongHun; Jo, YoungJu; Kim, Young Seo; Park, Weisun; Min, Hyun-seok; Park, YongKeun, OPTICS EXPRESS, v.27, no.4, pp.4927 - 4943, 2019-02 |
Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning Jo, YoungJu; Cho, Hyungjoo; Park, Wei Sun; Kim, Geon; Ryu, DongHun; Kim, Young Seo; Lee, Moosung; et al, NATURE CELL BIOLOGY, v.23, no.12, pp.1329 - 1337, 2021-12 |
Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells Kim, Geon; Jo, YoungJu; Cho, Hyungjoo; Min, Hyun-seok; Park, YongKeun, BIOSENSORS AND BIOELECTRONICS, v.123, pp.69 - 76, 2019-01 |
Quantitative Phase Imaging and Artificial Intelligence: A Review Jo, YoungJu; Cho, Hyungjoo; Lee, Sang Yun; Choi, Gunho; Kim, Geon; Min, Hyun-seok; Park, YongKeun, IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, v.25, no.1, pp.6800914, 2019-01 |
Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network Kim, Geon; Ahn, Daewoong; Kang, Minhee; Park, Jinho; Ryu, DongHun; Jo, YoungJu; Song, Jinyeop; et al, LIGHT-SCIENCE & APPLICATIONS, v.11, no.1, 2022-06 |
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