Papers
3
Total Citations
31
H-Index
3
About
Beibei Jiang is a researcher at the forefront of integrating artificial intelligence with medical imaging and industrial manufacturing. Her primary research areas include deep learning, computer vision, and reinforcement learning, with a focus on motion artifact correction and process optimization. Jiang’s most impactful work, “Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study” (2019, 22 citations), introduces a CNN-based method to correct motion-induced errors in coronary calcium scoring, a critical metric for cardiovascular risk assessment. This contribution addresses a longstanding challenge in cardiac CT imaging, demonstrating how AI can enhance diagnostic accuracy. She further explored this domain in her 2021 study (4 citations), which classifies motion artifacts in calcified plaques and identifies key factors influencing CNN performance. Beyond medicine, Jiang applies machine learning to industrial automation, as seen in her 2020 paper (5 citations) on using deep learning to enable reward signals for reinforcement learning in laser beam welding. This work advances the vision of self-optimizing robots in future factories. Jiang’s research bridges healthcare and manufacturing, showcasing the versatility of AI in solving real-world problems.
Research Focus
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