Xiangde Luo
Postdoctoral Scholar at Stanford University
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolXiangde Luo is a postdoctoral scholar based in Palo Alto with a decade of experience developing annotation-efficient medical imaging AI for clinical use, particularly in radiotherapy. He builds practical tools and public resources—most notably the widely adopted SSL4MIS semi-supervised segmentation benchmark (2k+ stars) and the large-scale WORD abdominal organ dataset—and has organized community challenges like SegRap2023. His work spans self-, semi-, and weakly-supervised methods, active learning, and human-in-the-loop systems, and has garnered significant scholarly impact (≈4,800 citations, h-index 19). Comfortable bridging research and engineering, he frequently contributes code and training optimizations to open-source projects, reflecting a hands-on focus on making robust models reproducible and clinically applicable.
9 years of coding experience
Ph.D. student, Medical Image Computing, Ph.D. student, Medical Image Computing at University of Electronic Science and Technology of China
Github Skills (8)
net10
mask-rcnn10
faster-rcnn10
computer-vision10
machine-learning10
pytorch10
medical-image-segmentation10
semi-supervised-learning10
Programming languages (4)
Github contributions (5)
HiLab-git/SSL4MIS
Sep 2020 - Jan 2023
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
Role in this project:
ML Engineer
ML EngineerContributions:432 commits, 8 PRs, 455 pushes in 2 years 3 months
Contributions summary:Xiangde primarily contributed to the development and training of semi-supervised learning models for medical image segmentation. Their commits involve modifications to training scripts for various models (U-Net variants), indicating experimentation with different semi-supervised approaches like entropy minimization, mean teacher models, and uncertainty-aware methods. Code changes include adjustments to learning rates, patch sizes, and loss functions, showing a focus on optimizing the training process.
pytorchimplementationssupervised-learningsupervisedmedical-image
HiLab-git/WSL4MIS
Dec 2020 - Dec 2022
Scribbles or Points-based weakly-supervised learning for medical image segmentation, a strong baseline, and tutorial for research and application.
Contributions:52 commits, 1 PR, 52 pushes in 2 years
medical-image-segmentationweakly-supervised-learningweakly-supervised-segmentation
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