Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI

Kexin Jiang(Third Affiliated Hospital of Southern Medical University), Yuhan Xie(Sun Yat-sen University), Xintao Zhang(Third Affiliated Hospital of Southern Medical University), Xinru Zhang(Third Affiliated Hospital of Southern Medical University), Beibei Zhou(Third Affiliated Hospital of Southern Medical University), Mianwen Li(Third Affiliated Hospital of Southern Medical University), Yanjun Chen(Third Affiliated Hospital of Southern Medical University), Jiaping Hu(Third Affiliated Hospital of Southern Medical University), Zhiyong Zhang(Sun Yat-sen University), Shao‐Long Chen(Sun Yat-sen University), Keyan Yu(Third Affiliated Hospital of Southern Medical University), Changzhen Qiu(Sun Yat-sen University), Xiaodong Zhang(Third Affiliated Hospital of Southern Medical University)
Journal of Imaging Informatics in Medicine
July 17, 2024
10.1007/s10278-024-01198-4
Cited by 9Open Access
Full Text

Abstract

Related Papers

Deep Learning: A Primer for Radiologists
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov et al.|Radiographics|2017|1.1k
The knee meniscus: Structure–function, pathophysiology, current repair techniques, and prospects for regeneration
Eleftherios Makris, Pasha Hadidi, Kyriacos A. Athanasiou|Biomaterials|2011|1.1k
Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score)
David J. Hunter, A. Guermazi, Grace H. Lo et al.|Osteoarthritis and Cartilage|2011|1.1k
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Nicholas Bien, Pranav Rajpurkar, Robyn L. Ball et al.|PLoS Medicine|2018|725
Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal
Steven Guan, Amir Khan, Siddhartha Sikdar et al.|IEEE Journal of Biomedical and Health Informatics|2019|535