教育经历:
2012 Ph.D.，University of Michigan, Ann Arbor
2009 M.S.E.，University of Michigan, Ann Arbor
学术简历 :
2019 – Biomedical Engineering Department , Peking University
2016-2019 Shanghai Jiao Tong University
2012-2016 Postdoctoral Research Fellow, University of Michigan, Ann Arbor
研究领域：
Biomechanics；Biomedical Imaging；Deep Learning
获得荣誉：
2021 PKU-Baidu Fund,Peking University
2020，2021 Clinical Medicine+ X Young Investigator Grant Award, Peking University
2014 MICHR PTSP Career Development Award, University of Michigan, Ann Arbor
近5年代表性论文：
1.He D, Zhou JS, Shang XY, Tang XY, Luo JJ*, Chen SL* (2023). De-noising of Photoacoustic Microscopy Images by Attentive Generative Adversarial Network. IEEE Transactions on Medical Imaging, DOI: 10.1109/TMI.2022.3227105.
2.Luo JJ*, Swenson CW, Betschart C, Feng F, Wang H, Ashton-Miller JA, DeLancey JOL (2023). Comparison of in vivo Visco-Hyperelastic Properties of Uterine Suspensory Tissue in Women with and without Pelvic Organ Prolapse. Journal of the Mechanical Behavior of Biomedical Materials, 137: 105544.
3.Edwards LA, Feng F, Iqbal M, Fu Y, Sanyahumbi A, Hao S, McElhinney DB, Ling XB, Sable C, Luo JJ* (2022). Machine Learning for Pediatric Echocardiographic Mitral Regurgitation Detection. Journal of the American Society of Echocardiography, DOI: doi.org/10.1016/j.echo.2022.09.017.
4.He D, Shang XY, Luo JJ* (2022). Adherent Mist and Raindrop Removal from a Single Image Using Attentive Convolutional Network. Neurocomputing, 505: 178-187.
5.Feng F, Liang SQ, Luo JJ*, Chen SL* (2022). High-Fidelity Deconvolution for Acoustic-Resolution Photoacoustic Microscopy Enabled by Convolutional Neural Networks. Photoacoustics, 26:100360.
6.Feng F, Ashton-Miller JA, DeLancey JOL, Luo JJ* (2022). Three-dimensional Self Super-Resolution for Pelvic Floor MRI Using a Convolutional Neural Network with Multi-Orientation Data Training. Medical Physics, 49:1083–1096.
7.Wang XY, He D, Feng F, Ashton-Miller JA, DeLancey JOL, Luo JJ* (2021). Multi-label Classification of Pelvic Organ Prolapse Using Stress Magnetic Resonance Imaging with Deep Learning. Int Urogynecol J, DOI: 10.1007/s00192-021-05064-7.
8.Zhou JS, He D, Shang XY, Guo ZD, Chen SL*, Luo JJ* (2021). Photoacoustic Microscopy with Sparse Data by Convolutional Neural Networks. Photoacoustics, 22:100242.
9.Feng F, Ashton-Miller JA, DeLancey JOL, Luo JJ* (2021). Feasibility of a Deep Learning-based Method for Automated Localization of Pelvic Floor Landmarks Using Stress MR Images. Int Urogynecol J, 32(11): 3069-3075.
10.He D, Cai D, Zhou JS, Luo JJ*, Chen SL* (2020). Restoration of Out-of-Focus Fluorescence Microscopy Images Using Learning-Based Depth-Variant Deconvolution. IEEE Photonics Journal, 12(2): 1-13.
11.Feng F, Ashton-Miller JA, DeLancey JOL, Luo JJ* (2020). Convolutional Neural Network-Based Pelvic Floor Structures Segmentation Using Magnetic Resonance Imaging in Pelvic Organ Prolapse. Medical Physics, 47(9): 4281-4293.
12.Luo JJ*, Chen L, Fenner DE, Ashton-Miller JA, DeLancey JOL (2015). A Multi-compartment 3-D Finite Element Model of Rectocele and Its Interaction with Cystocele. J Biomech, 48(9), 1580-1586.