Hui Lin (林惠)
![prof_pic.jpg](/assets/img/prof_pic.jpg)
Tech M470
2145 Sheridan Road
Evanston, IL 60208
I am a Ph.D. candidate at the iVPL lab at Northwestern University, supervised by Prof. Aggelos Katsaggelos. My research focuses on deep learning and computer vision-based detection, segmentation, and generation in the areas of manufacturing and medical imaging. My first two years were spent studying automatic defect detection and thermal prediction during additive manufacturing; then, I worked on organ segmentation and unsupervised domain adaptation in MRI, CT, OCT, X-ray, etc.
Previously, I obtained a master’s degree in Mechanical Engineering from Huazhong University of Science and Technology (HUST) in 2019, supervised by Prof. Bin Li and Prof. Xinggang Wang. I received a Bachelor’s degree in Materials Processing and Control Engineering from Qiming College, HUST, in 2016.
news
Jun 3, 2024 | I am doing a research internship in the Health Lab at OPPO US Research Center this summer! The interesting research topic is DL-based blood pressure monitoring using PPG signals from wearables. |
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May 28, 2024 | We were ranked 5th among all 324 participants in the Justified Referral in AI Glaucoma Screening (JustRAIGS) challenge at ISBI 2024 ! |
Mar 29, 2024 | The paper Usformer 2.0 was accepted by Heliyon. |
Oct 15, 2023 | We were ranked 3rd in the task of coronary artery segmentation in the Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) challenge at MICCAI 2023 ! |
Oct 15, 2023 | We were ranked 3rd in the task of stenosis detection in the ARCADE challenge at MICCAI 2023! |
selected publications
2024
2023
- MICAAI Chall.YOLO-Angio: An Algorithm for Coronary Anatomy SegmentationarXiv preprint arXiv:2310.15898, 2023
- MICAAI Chall.StenUNet: Automatic Stenosis Detection from X-ray Coronary AngiographyarXiv preprint arXiv:2310.14961, 2023
2020
- IEEE TASEDefect image sample generation with GAN for improving defect recognitionIEEE Transactions on Automation Science and Engineering, 2020
2019
- J. Intell. Manuf.Automated defect inspection of LED chip using deep convolutional neural networkJournal of Intelligent Manufacturing, 2019