My research mainly focuses on the clinical translation of advanced deep learning and MRI techniques.
Research activities
Publications
Expand the titles for my publications.
- Li S, Yao Y, Zhong J, et al. ERANet: Edge replacement augmentation for semi-supervised meniscus segmentation with prototype consistency alignment and conditional self-training. Neural Networks. 2026;196:108337. doi:10.1016/j.neunet.2025.108337
- Khan S, Khawer MA, Zhong J, et al. Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications. Osteoarthritis and Cartilage Open. 2026;8(1):100702. doi:10.1016/j.ocarto.2025.100702
- Zhong J, Huang C, Yu Z, et al. Utilizing 3D Fast Spin Echo Anatomical Imaging to Reduce the Number of Contrast Preparations in T1ρ Quantification of Knee Cartilage Using Learning-Based Methods. Magnetic Resonance in Medicine. 2025. doi: 10.1002/mrm.70022.
- Zhong J, Yao Y, Xiao F, et al. A systematic automated post-processing approach for quantitative analysis of 3D T1ρ knee MRI. arXiv: 2409.12600
- Yao Y, Zhong J, Zhang L, et al. CartiMorph: A framework for automated knee articular cartilage morphometrics. Medical Image Analysis. 2024 Jan 1;91:103035.
- Zhong J, Yao Y, Cahill DG, et al. Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification. Quantitative Imaging in Medicine and Surgery. 2023 Oct 17;13(11):7444–7458.
- Zhong J, Chow JTH, Li KY, et al. 220P Exploring large language model (LLM) for TNM categorizing and re-categorizing nasopharyngeal carcinoma (NPC) from structured text reports. ESMO Real World Data and Digital Oncology. 2025;10:100416. doi:10.1016/j.esmorw.2025.100416
- Zhong J*, Yao Y*, Xiao F, et al. A SYSTEMATIC POST-PROCESSING APPROACH FOR T1ρ IMAGING OF KNEE ARTICULAR CARTILAGE. In: 19th International Workshop on Osteoarthritis Imaging, Cambridge, United Kingdom, July 9 -12, 2025. Osteoarthritis Imaging 5 (2025) 100275.
- Zhong J, Huang C, Yu Z, et al. Utilization of Clinical Knee MRI to Accelerate Quantitative T1ρ Imaging of Knee. In: Proceeding of the International Society for Magnetic Resonance in Medicine. Honolulu, Hawaiʻi, USA; 2025.
- Shen Q, Wong V, Zhong J, et al. Deep learning enabled motion detection in quantitative macromolecule proton faction mapping in the liver. In: Proceeding of the International Society for Magnetic Resonance in Medicine. Honolulu, Hawaiʻi, USA; 2025.
- Zhong J, Yao Y, Xiao F, et al. A systematic automated post-processing approach for quantitative analysis of 3D T1ρ knee MRI. In: Proceeding of the International Society for Magnetic Resonance in Medicine. Singapore; 2024.
- Zhong J, Yao Y, Cahill DG, et al. Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification. In: Proceeding of the International Society for Magnetic Resonance in Medicine. Toronto, ON, Canada; 2023.
- Zhong J, Yao Y, Khan S, et al. Knee Osteoarthritis: Automatic Grading with Deep Learning. In: Proceeding of the International Society for Magnetic Resonance in Medicine. London, England, UK; 2022.
- Li S, …, Zhong J, et al. Unsupervised Domain Adaptation via CycleGAN for knee joint Segmentation in MR Images. In: Proceeding of the International Society for Magnetic Resonance in Medicine. London, England, UK; 2022.
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Publications
11 publications