QI CHANG, Ph.D

About Me

I have earned my Ph.D. in Computer Science from Rutgers University under the supervision of Professor Dimitris Metaxas. Before that, I received an M.S degree from Graduate Univ. of Chinese Academy of Sciences and obtained a B.E degree from University of Electronic Science and Technology of China.
I worked at Cleerly, LightInTheBox, and Baidu.
My research interests include machine learning and computer vision, involving the topics:
  • Perception: Image Segmentation and Classification
  • Generative Adversarial Network: Inversion, Image Manipulation, Motion Adaptation, Federated GAN
  • Medical Image Processing: Cardiac Image Analysis in CT(3D) and MRI(4D) Images

Work Experiences

  • Cleerly Inc. Chief Data Scientist, NYC, NY. (July,2017-July,2019)
  • Weill Cornell Medical College-Cornell University. Machine Learning Research Intern, NYC, NY. (2016 Summer)
  • LightInTheBox. Senior Technical Manager, Beijing. (2012-2014)
  • Baidu. Senior Engineer, Technical Manager, Beijing. (2009-2012)

Projects

Cleerly Lab - Lead the Cleerly Data Science Team to build the end2end ML based image analysis solution of CT Angiography as the backbone of the advanced FDA clearance device.
ATMI - ML-integrated, Web-based, audit-enabled, mobile device friendly, one click installs, easy to use medical annotation tool.
Baidu Image Mobile WebApp - Designed and implemented the new advantage version of image search pages, well compatible with iOS and android system and optimize the picture loading performance, picture switch performance.

Selected Research

Qi Chang, Zhennan Yan, Mu Zhou, and Dimitris Metaxas. DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method. Manuscript submitted for publication
Qi Chang, Zhennan Yan, Hui Qu, Han Zhang, Lonhendran Baskaran, Shaoting Zhang and Dimitris N. Metaxas. Federated Synthetic Learning from Multi-institutional and Heterogeneous Medical Data
Chang, Qi, Zhennan Yan, Meng Ye, Kanski Mikael, Subhi Al’Aref, Leon Axel, and Dimitris N. Metaxas. "An Unsupervised 3D Recurrent Neural Network for Slice Misalignment Correction in Cardiac MR Imaging." In International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 141-150. Springer, Cham, 2021.(MICCAIw)
Meng Ye, Mikael Kanski, Dong Yang, Qi Chang, Zhennan Yan, Qiaoying Huang, Leon Axel, Dimitris Metaxas. “DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),2021
Qi Chang, Hui Qu, Yikai Zhang, Mert Sabuncu, Chao Chen, Tong Zhang, and Dimitris N. Metaxas. "Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13856-13866. 2020.
Hui Qu*, Yikai Zhang*, Qi Chang*(equal contribution), Zhennan Yan, Chao Chen and Dimitris Metaxas. “Learn distributed GAN with Temporary Discriminators” European Conference on Computer Vision (ECCV 2020)
Qi Chang, Zhennan Yan, Yixuan Lou, Leon Axel, and Dimitris N. Metaxas. "Soft-Label Guided Semi-Supervised Learning for Bi-Ventricle Segmentation in Cardiac Cine MRI." In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1752-1755. IEEE, 2020.