Ma Tin Lay Nwe
Papers
1
Total Citations
18
H-Index
1
About
Dr. Ma Tin Lay Nwe is a pioneering researcher at the intersection of computer vision, deep learning, and non-destructive evaluation. Her work focuses on developing automated methods for analyzing 3D X-ray imagery, with a particular emphasis on detecting and measuring buried features in complex volumetric data. Her most cited paper, "Automated Attribute Measurements of Buried Package Features in 3D X-ray Images using Deep Learning" (2021, 18 citations), exemplifies her innovative approach—adapting state-of-the-art deep learning models, originally designed for medical imaging and robotics, to the challenging domain of industrial inspection. In this work, she successfully trained models to identify and segment structures like Through-Hole components, demonstrating how transfer learning can solve real-world problems in quality control and security screening. Dr. Nwe’s contributions are notable for bridging the gap between advanced AI techniques and practical engineering applications, offering scalable solutions for automated analysis of buried objects. Her research has significant implications for manufacturing, security, and materials science, where precise, non-invasive inspection is critical. With a growing citation record, Dr. Nwe is establishing herself as a key figure in applied deep learning for 3D image analysis.
Research Focus
Key Achievements
Top Papers
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