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Scientific Reports|December 31, 2024
An effective method for detecting the wheat freshness by integrating biophotonics and machine learning algorithmWeiya Shi, Liang Chen
Journal of Photochemistry and Photobiology. B, Biology|January 18, 2016
Efficient detection of internal infestation in wheat based on biophotonicsWeiya Shi, Keke Jiao, Yitao Liang, et al.
Interdisciplinary Sciences, Computational Life Sciences|December 12, 2025
EASNet: Edge-aware Segmentation Network for Skin Lesion Segmentation with Boundary-aware and Frequency Attention MechanismsJunwei Yu, Yuhe Xia, Jianping Li, et al.
IEEE Journal of Biomedical and Health Informatics|October 13, 2020
M <sup>3</sup>Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT ImagingXuelin Qian, Huazhu Fu, Weiya Shi, et al.
Quantitative Imaging in Medicine and Surgery|January 21, 2020
An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growingHe Ren, Lingxiao Zhou, Gang Liu, et al.
Medical Physics|November 23, 2020
Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity predictionFei Shan, Yaozong Gao, Jun Wang, et al.
Journal of Thoracic Disease|August 3, 2019
HIV-infected patients with opportunistic pulmonary infections misdiagnosed as lung cancers: the clinicoradiologic features and initial application of CT radiomicsWeiya Shi, Lingxiao Zhou, Xueqing Peng, et al.
The American Journal of the Medical Sciences|May 16, 2017
The Association Between Endocan Levels and Subclinical Atherosclerosis in Patients With Type 2 Diabetes MellitusYaya Lv, Yaqiong Zhang, Weiya Shi, et al.
Frontiers in Oncology|May 10, 2021
Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma <i>In Situ</i> and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in DiameterLili Shi, Weiya Shi, Xueqing Peng, et al.
Annals of Translational Medicine|March 12, 2021
A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patientsWeiya Shi, Xueqing Peng, Tiefu Liu, et al.
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