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Sijie Niu

Showing results (1-10 of 44) with videos related to

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Machine Learning in Medical Imaging. MLMI (Workshop)|April 26, 2022
Informative Feature-Guided Siamese Network for Early Diagnosis of AutismKun Gao, Yue Sun, Sijie Niu, et al.
Frontiers in Medicine|August 15, 2024
Editorial: Generalizable and explainable artificial intelligence methods for retinal disease analysis: challenges and future trendsQiang Chen, Theodore Leng, Sijie Niu, et al.
Autism Research : Official Journal of the International Society for Autism Research|October 13, 2021
Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imagingKun Gao, Yue Sun, Sijie Niu, et al.
Medical Physics|April 3, 2016
Choroidal vasculature characteristics based choroid segmentation for enhanced depth imaging optical coherence tomography imagesQiang Chen, Sijie Niu, Songtao Yuan, et al.
Medical Physics|October 27, 2016
High-low reflectivity enhancement based retinal vessel projection for SD-OCT imagesQiang Chen, Sijie Niu, Songtao Yuan, et al.
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|September 16, 2025
Adaptive Anchor-Guided Representation Learning for Efficient Multi-View Subspace ClusteringMengjiao Zhang, Xinwang Liu, Tianhao Han, et al.
Translational Vision Science & Technology|January 6, 2018
Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT ImagesZexuan Ji, Qiang Chen, Sijie Niu, et al.
Computers in Biology and Medicine|January 4, 2019
Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning modelRongbin Xu, Sijie Niu, Qiang Chen, et al.
Optics Express|May 14, 2015
Automated choroid segmentation based on gradual intensity distance in HD-OCT imagesQiang Chen, Wen Fan, Sijie Niu, et al.
Machine Learning in Medical Imaging. MLMI (Workshop)|May 9, 2022
Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs ArtifactsYue Sun, Kun Gao, Weili Lin, et al.
Pageof 5

Showing results (1-10 of 44) with videos related to

Sort By:
Pageof 5
Machine Learning in Medical Imaging. MLMI (Workshop)|April 26, 2022
Informative Feature-Guided Siamese Network for Early Diagnosis of AutismKun Gao, Yue Sun, Sijie Niu, et al.
Frontiers in Medicine|August 15, 2024
Editorial: Generalizable and explainable artificial intelligence methods for retinal disease analysis: challenges and future trendsQiang Chen, Theodore Leng, Sijie Niu, et al.
Autism Research : Official Journal of the International Society for Autism Research|October 13, 2021
Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imagingKun Gao, Yue Sun, Sijie Niu, et al.
Medical Physics|April 3, 2016
Choroidal vasculature characteristics based choroid segmentation for enhanced depth imaging optical coherence tomography imagesQiang Chen, Sijie Niu, Songtao Yuan, et al.
Medical Physics|October 27, 2016
High-low reflectivity enhancement based retinal vessel projection for SD-OCT imagesQiang Chen, Sijie Niu, Songtao Yuan, et al.
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|September 16, 2025
Adaptive Anchor-Guided Representation Learning for Efficient Multi-View Subspace ClusteringMengjiao Zhang, Xinwang Liu, Tianhao Han, et al.
Translational Vision Science & Technology|January 6, 2018
Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT ImagesZexuan Ji, Qiang Chen, Sijie Niu, et al.
Computers in Biology and Medicine|January 4, 2019
Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning modelRongbin Xu, Sijie Niu, Qiang Chen, et al.
Optics Express|May 14, 2015
Automated choroid segmentation based on gradual intensity distance in HD-OCT imagesQiang Chen, Wen Fan, Sijie Niu, et al.
Machine Learning in Medical Imaging. MLMI (Workshop)|May 9, 2022
Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs ArtifactsYue Sun, Kun Gao, Weili Lin, et al.
Pageof 5