Qiuwen Wu
Papers
3
Total Citations
38
H-Index
3
About
Qiuwen Wu is a leading researcher at the intersection of artificial intelligence and radiation oncology, whose work is reshaping how cancer treatments are planned and delivered. Her primary research areas include AI-driven treatment planning optimization, machine learning applications in radiotherapy, and computational methods for robotic radiosurgery systems. Wu’s most impactful contribution is her comprehensive overview of artificial intelligence applications in intensity-modulated radiation treatment planning, which has garnered 26 citations and serves as a foundational reference for researchers integrating AI into clinical workflows. She has also pioneered novel computational techniques, including a PyTorch-based toolkit for optimizing circular cone robotic radiotherapy—addressing the critical challenge of prolonged planning times in non-coplanar treatment spaces—and a singular value decomposition linear programming (SVDLP) method that improves optimization efficiency for CyberKnife systems. Her work directly tackles the complexity of robotic radiotherapy delivery, offering practical solutions that reduce treatment planning time while maintaining plan quality. With a growing citation record and a focus on translating AI advances into tangible clinical improvements, Wu is establishing herself as a key innovator in the rapidly evolving field of automated radiation treatment planning.
Research Focus
Key Achievements
Top Papers
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