Peishan Dai, associate professor,  Doctoral supervisor of computer science and engineering,.

Professional Experience:

August, 2012 to August, 2013: visiting scholar of the University of Chicago. Published several academic papers; Presided over the National Natural Science Foundation of China, Hunan Natural Science Foundation, etc., participated in 2 projects of the National Natural Science Foundation of China, and a number of provincial and ministerial projects; Reviewer of NSFC; Reviewer of IEEE Transactions on medical imaging, Translational psychiatry, International Journal for computational methods in Engineering Science & mechanics, Journal of automation, Journal of Biomedical Engineering, Chinese medical physics, etc.

Research Specialization:

Signal and image processing, machine learning,deep learning, pattern recognition and statistical analysis and their applications in medical image processing.

Email:

daipeishan@163.com

Publications：

1. Classification of MDD using a Transformer classifier with large-scale multi-site resting-state fMRI data, Human Brain Mapping (accepted).

2. Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fMRI data, Journal of Affective Disorders, 2023.

3. Semi-supervised OCT lesion segmentation via transformation-consistent with uncertainty and self-deep supervision, Biomedical Optics Express, 2023.

4. AC-E Network: Attentive Context-Enhanced Network for Liver Segmentation, IEEE Journal of Biomedical and Health Informatics, 2023.

5. Marginal samples for knowledge distillation, Neurocomputing, 2022, 507.

6. The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data., Behavioural Brain Research, 2022, 435: 114058.     

7. Altered Effective Connectivity Among the Cerebellum and Cerebrum in Patients with Major Depressive Disorder Using Multisite Resting-State fMRI., Cerebellum, 2022.   

8. A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis., Phys Eng Sci Med, 2022, 45(3): 867-882. 

9. Altered Effective Connectivity of Children and Young Adults With Unilateral Amblyopia: A Resting-State Functional Magnetic Resonance Imaging Study, Frontiers in Neuroscience, 2021, 15: 657576.    

10. DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images, Biomedical Signal Processing and Control, 2020, 55: 101632.    

11. Altered Spontaneous Brain Activity of Children with Unilateral Amblyopia: A Resting State fMRI Study, NEURAL PLASTICITY, 2019, 2019: 3681430.    

12. Automatic segmentation for cell images based on bottleneck detection and ellipse fitting, Neurocomputing, 2016, 173(Part 3): 615-622.  

13. A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model, PLos One, 2015, 10(6): e0127748.  

14. Constructing three-dimensional detachable and composable computer models of the head and neck, Australasian Physical & Engineering Sciences in Medicine, 2015, 38(2): 271-281.    

15. SIMULATING THE EFFECTS OF ELEVATED INTRAOCULAR PRESSURE ON OCULAR STRUCTURES USING A GLOBAL FINITE ELEMENT MODEL OF THE HUMAN EYE, Journal of Mechanics in Medicine and Biology, 2017, 2016(2): 1750038.    

16. FINITE ELEMENT ANALYSIS OF THE MECHANICAL CHARACTERISTICS OF GLAUCOMA, Journal of Mechanics in Medicine and Biology, 2016, 16(2): 1650060.    

17. Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretching, Optics and Laser Technology, 2014, 58: 56-62. 

18. Constructing a computer model of the human eye based on tissue slice images, International Journal of Biomedical Imaging, 2010, 2010.  

19. 视网膜血管图像分割及眼底血管三维重建. 自动化学报, (09).