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Showing results 1 to 15 of 15

	Title	Author(s)	Issue Date
	5-Year progression prediction of endplate defects: Utilizing the EDPP-Flow convolutional neural network based on unbalanced data Journal:Journal of Orthopaedics	Cheung, JPY KUANG, X Zhang, T Wang, K Yang, C	2023
	A novel tool to provide predictable alignment data irrespective of source and image quality acquired on mobile phones: what engineers can offer clinicians Proceeding/Conference:ISSLS (International Society for the Study of the Lumbar Spine) Virtual Annual Meeting	Zhang, T Kuang, X Zhu, C Lu, Q Liu, J Diwan, A Cheung, JPY	2021
	Accurate Cobb Angle Prediction via Learning Auxiliary Tasks	Zhang, T Meng, N Zhao, M Kuang, X Cheung, JPY	24-Jul-2023
	Deep learning based fully automated pathology classification of lumbar spine Proceeding/Conference:ISSLS (International Society for the Study of the Lumbar Spine) Virtual Annual Meeting 2021	Kuang, X Cheung, JPY Zhang, T	2021
	Deep learning-based fully automated vertebral endplates irregularity prediction using lumbar magnetic resonance imaging Proceeding/Conference:41st Annual Congress of the Hong Kong Orthopaedic Association 2021	KUANG, X Cheung, JPY Zhang, T	2021
	Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation Journal:IEEE Transactions on Medical Imaging	LI, Y ZHANG, Y CUI, W LEI, B KUANG, X Zhang, T	2022
	Learning-based fully automated prediction of lumbar disc degeneration progression with specified clinical parameters and preliminary validation Journal:European Spine Journal	Cheung, JPY KUANG, X Lai, KKL Cheung, KMC Karppinen, J Samartzis, D Wu, H Zhao, FD Zheng, ZM Zhang, T	2021
	Learning-based fully automated prediction of lumbar disc degeneration progression with specified clinical parameters and preliminary validation Proceeding/Conference:ISSLS (International Society for the Study of the Lumbar Spine) Virtual Annual Meeting 2021	Cheung, JPY Kuang, X Lai, M Cheung, K Karppinen, J Samartzis, D Wu, H Zhao, F Zheng, Z Zhang, T	2021
	MRI-SegFlow V2.0: a novel unsupervised deep learning pipeline enabling accurate semantic segmentation of lumbar MR images with preliminary validation Proceeding/Conference:ISSLS (International Society for the Study of the Lumbar Spine) Virtual Annual Meeting 2021	Kuang, X Cheung, JPY Wu, H Lam, C Choy, R Chan, D Zhang, T	2021
	MRI-SegFlow: a deep learning-based unsupervised pipeline for vertebral segmentation of spinal MRI image Proceeding/Conference:40th Annual Congress of the Hong Kong Orthopaedic Association 2020	Kuang, X Cheung, JPY Wu, H Dokos, S Zhang, T	2020
	MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images Proceeding/Conference:IEEE Engineering in Medicine and Biology Society (EMBC) Conference Proceedings	Kuang, X Cheung, JPY Wu, H Dokos, S Zhang, T	2020
	Spine-GFlow: A hybrid learning framework for robust multi-tissue segmentation in lumbar MRI without manual annotation Journal:Computerized Medical Imaging and Graphics	KUANG, X Cheung, JPY Wong, KKY Lam, WY Lam, CH Choy, RW Cheng, CP Wu, H Yang, C Wang, K Li, Y Zhang, T	2022
	Spine-SegLoop: A Deep Learning Framework for Multi-tissue Segmentation in Lumbar MRI with No Manual Annotations Proceeding/Conference:48th ISSLS Annual Meeting, 2022	KUANG, X Cheung, JPY Wong, KKY Zhang, T	2022
	SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset Journal:Medical Image Computing and Computer Assisted Intervention - MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12902. Springer, Cham	KUANG, X Cheung, JPY Ding, XW Zhang, T	2021
	SpineQ: Unsupervised Learning-Based Pipeline for Fully Automated Quantitative Analysis of Lumbar MRI With Preliminary Validation	Zhang, T Kuang, X Ke, WC Yang, C Cheung, JPY	1-May-2023

Showing results 1 to 15 of 15