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10:30 - 10:45
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Manuscript ID. 0222
Paper No. 2020-SAT-S0603-O001
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Yu-Sheng Chi
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Assisted Diagnosis of Esophageal Cancer with Hyperspectral Imaging and Convolutional Neural Network
Yu-Sheng Chi;I-Chen Wu;Yao-Kuang Wang;Tsung-Yu Yang;Hsiang-Chen Wang
Two kinds of data: white-light (WLI), narrow-band (NBI) spectrum data from their endoscopic images, can be divided into four categories, Normal, Dysplasia, Dysplasia-ECA and ECA. Then, trained through the Single Shot Multibox Detector (SSD) with convolutional neural network (CNN). The precisions corresponding to stages are 85%, 84%, 88%, and 92%.
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10:45 - 11:00
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Manuscript ID. 0605
Paper No. 2020-SAT-S0603-O002
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Chi-Hao Luo
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Multi-depth imaging by SAX microscopy
Chi-Hao Luo;Kuang-Yuh Huang;Yuan Luo
Saturated excitation microscopy (SAX) can provide high spatial resolution images with the help of higher order harmonic frequencies. However, obtaining images at different depth is a challenging task. Here, we show multi-depth imaging in SAX microscope using tunable focusing lens without moving samples or microscope objective.
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11:00 - 11:15
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Manuscript ID. 0776
Paper No. 2020-SAT-S0603-O003
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Sandeep Chakraborty
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High-Speed, GRIN-lens Based, Two-Photon Fluorescence Microendoscope for Deep Brain Functional Imaging
Sandeep Chakraborty;Han-Wee Chong;Jieh-Sheng Hsu;Po-Ting Yeh;Shih-Kuo Chen;Ming-Jang Chiu;Chi-Kuang Sun
In this study, we have developed a high-resolution two-photon fluorescence microendoscope, based on GRIN lens, to observe longitudinally the neuronal activity in the deep brain of awake mice. Furthermore, with ultrahigh 2 kHz frame acquisitions, the neuronal activity of diseased brain vs. normal brain can be easily differentiated.
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11:15 - 11:30
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Manuscript ID. 0600
Paper No. 2020-SAT-S0603-O004
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Hua-Wei Ku
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Image Denoising of Temporal Focusing Multiphoton Microscopy via Cascade 3D Deep Learning
Hua-Wei Ku;Xin-Ni Huang;Feng-Chun Hsu;Yvonne Yuling Hu;Chun-Yu Lin;Hsueh-Cheng Chiang;Shean-Jen Chen
Temporal focusing multiphoton excitation microscopy is potential to provide fast bio-imaging. Its surpassing acquisition rate is better than extensively used two-photon excitation microscopy, but the scattering phenomenon induces much high noise. To improve image quality, we induce deep learning method and present cascade 3D U-Net to realize high-speed 3D imaging.
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11:30 - 11:45
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Manuscript ID. 0244
Paper No. 2020-SAT-S0603-O005
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THAI HONG NGUYEN
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An examination of Malignant Pleural Effusion by Hyperspectral Imaging Technology
THAI HONG NGUYEN
Pleural fluid cytology serves as useful information in diagnostic lung cancer. In this study, we propose a model for analyzing pleural effusion cytology images based on hyperspectral imaging technology. The different hyperspectral spectra features are studied. We expect the proposed method could assist doctors in detecting lung cancer rapidly.
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11:45 - 12:00
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Manuscript ID. 0585
Paper No. 2020-SAT-S0603-O006
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CHI-YU WANG
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To Improve the Axial Resolution of Rapid Volumetric Multiphoton Microscopy via 3D U-Net
CHI-YU WANG;Chia-Wei Hsu;Chun-Yu Lin;Shean-Jen Chen
In this study, we use a 3D U-Net to improve the axial resolution of rapid volumetric multiphoton microscopy without reducing volume rate. Currently, 30 volume rates can be kept with high image quality.
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