Description of Research Expertise:
Dr. Fan has a broad background in medical image analysis and pattern recognition, with specific training in applied mathematics, statistics, and machine learning.
His research interests are in the field of imaging analytics, machine learning, pattern recognition, and more generally in computational imaging. Much of his work has been focusing on methodology development and applications of machine learning techniques that quantify morphology and function from medical images, integrate multimodal information to aid diagnosis and prediction of clinical outcomes, and guide personalized treatments. The methodological focus has been on the general field of artificial intelligence, with emphasis on machine learning methods applied to complex and large imaging and clinical data. The image analytic methods being and to be developed include functional connectomics, radiomics, image registration and segmentation, and personalized neuromodulatory therapies. On the clinical side, his primary focus is on applications in clinical neuroscience, in cancer, and in chronic kidney disease, aiming to develop precision diagnostic tools using machine learning and pattern recognition techniques. The clinical research studies include brain development, brain diseases such as Alzheimer's, schizophrenia, depression, and addiction, pediatric kidney diseases, and predictive modeling of treatment outcomes of cancer patients such as rectal and lung cancers.
Hongming Li, Yong Fan: Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks Neuroimage 202 (15): 1-11,2019.
Hongming Li, Mohamad Habes, David A. Wolk, Yong Fan: A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data Alzheimer's & Dementia: The Journal of the Alzheimer's Association 15 (8): 1059-1070,2019.
Rixing Jing, Peng Li, Zengbo Ding, Xiao Lin, Rongjiang Zhao, Le Shi, Hao Yan, Jinmin Liao, Chuanjun Zhuo, Lin Lu, Yong Fan: Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients Human Brain Mapping 40 (13): 3930-3939,2019.
Rixing Jing, Yongsheng Han, Hewei Cheng, Yongzhu Han, Kai Wang, Daniel Weintraub, Yong Fan: Altered large-scale functional brain networks in neurological Wilson’s disease Brain Imaging and Behavior : 1-11,2019.
Reagan R. Wetherill, Hengyi Rao, Nathan Hager, Jieqiong Wang, Teresa R. Franklin, Yong Fan: Classifying and Characterizing Nicotine Use Disorder with High Accuracy Using Machine Learning and Resting-State fMRI Addiction Biology 24 (4): 811-821,2019.
Qiang Zheng, Susan L. Furth, Gregory E. Tasian, Yong Fan: Computer aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features Journal of Pediatric Urology 15 (1): 75.e1-75.e7,2019.
Hongming Li, Maya Galperin-Aizenberg, Daniel Pryma, Charles B. Simone II, and Yong Fan: Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy Radiotherapy & Oncology 129 (2): 218-226,2018.
Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, and Yong Fan: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation Medical Image Analysis 43 : 98-111,2018.
Xiaofeng Zhu, Weihong Zhang, Yong Fan: A robust reduced rank graph regression method for neuroimaging genetics analysis Neuroinformatics 16 (3-4): 351-361,2018.
Hongming Li, Theodore D. Satterthwaite, and Yong Fan: Large-scale sparse functional networks from resting state fMRI Neuroimage 156 : 1-13,2017.