Bio

Email: Yunanwu2020(Replace this parenthesis with the @ sign)u.northwestern.edu

GitHub: https://github.com/YunanWu2168 

Medium: https://medium.com/@yunanwu2020

Yunan Wu is a current Ph.D. candidate in the Electrical Computer Engineering Department working in the Image and Video Processing Lab (IVPL) at Northwestern University. 

She received her B.S. in Biomedical Engineering at Southern Medical University (SMU) in China (2014-2018). Yunan completed her undergraduate thesis comparing 1D Convolutional Neural Networks (CNNs) with 2D CNNs in detecting ventricular fibrillation. The paper was published in 40th International Conference of the IEEE in Engineering Medicine and Biology Society (EMBS), 2018 [paper].

She received her M.S. in Biomedical Engineering at Northwestern University and the master thesis is using geometric Deep Learning on brain morphology to predict composite score of fluid intelligence. The paper is currently in review [bioRxiv].

She is currently a Ph.D. candidate in the Electrical Computer Engineering Department at Northwestern University and a lab member of of the Image & Video Processing Lab (IVPL).  Her research interests are in artificial intelligence healthcare, machine learning, deep learning and Computer Vision. She is involved in research projects:

  • Detection of head CT hemorrhage [details],
  • Weakly supervised Attention-based Multiple Instance Learning,
  • Fluid intelligence prediction using Graph-based CNNs [details],
  • Covid-19 fusion project.     

Hobbies: jogging, painting, and most importantly, mountain climbing!!

Publications   

1. Y. Wu, P. Besson, E. A. Azcona, S. K. Bandt, T. B. Parrish, H. C. Breiter, A. K. Katsaggelos, Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study. bioRxiv, in press, doi:10.1101/2020.10.14.331199. [paper]

2. E. A. Azcona, P. Besson, Y. Wu, A. Punjabi, A. Martersteck, A. Dravid, T. B. Parrish, S. K. Bandt, A. K. Katsaggelos, in Shape in Medical Imaging, M. Reuter, C. Wachinger, H. Lombaert, B. Paniagua, O. Goksel, I. Rekik, Eds. (Springer International Publishing, Cham, 2020), Lecture Notes in Computer Science, pp. 95–107. [paper]

3. R. M. Wehbe, J. Sheng, S. Dutta, S. Chai, A. Dravid, S. Barutcu, Y. Wu, D. R. Cantrell, N. Xiao, B. D. Allen, G. A. MacNealy, H. Savas, R. Agrawal, N. Parekh, A. K. Katsaggelos, DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large US Clinical Dataset. Radiology, 203511 (2020). [paper] [codes]

4. Y. Wu, G. M. White, T. Cornelius, I. Gowdar, M. H. Ansari, M. P. Supanich, J. Deng, Deep learning LI-RADS grading system based on contrast enhanced multiphase MRI for differentiation between LR-3 and LR-4/LR-5 liver tumors. Annals of Translational Medicine. 8, 701 (2020).   (internship during master) [paper]

5. Y. Wu, Go-selfies: A Fast Selfies Background Removal Method Using ResU-Net Deep Learning. in 2020 28th European Signal Processing Conference (EUSIPCO) 615–619 (2021). (class project during master) [paper] [codes]

6. Y. Wu, F. Yang, Y. Liu, X. Zha, S. Yuan, A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification. arXiv:1810.07088 [cs] (2018) (available at http://arxiv.org/abs/1810.07088). (undergraduate) [paper]