Motaz Alqaoud
Papers
2
Total Citations
16
H-Index
2
About
Motaz Alqaoud is a researcher at the forefront of applying deep learning to medical image analysis, with a focused expertise in breast cancer diagnosis and robotic surgery planning. His primary research areas include multi-modality medical image segmentation, deep neural network architectures, and the development of tools for preoperative surgical navigation. Alqaoud’s major contribution lies in adapting the state-of-the-art nnU-Net framework for the complex task of segmenting breast tissues and the thoracic region from multi-modality MRI scans. His work, notably detailed in his 2022 paper “nnUNet-based Multi-modality Breast MRI Segmentation,” which has garnered 14 citations, introduces a novel cascaded architecture of two neural networks to enhance segmentation accuracy. This innovation is critical for creating tissue-delineating phantoms that allow surgeons to plan robotic tumor excisions with greater precision. By establishing a robust foundation for preoperative segmentation, Alqaoud’s research directly bridges the gap between advanced computer vision and practical, life-saving clinical applications, marking him as a key contributor to the future of automated surgical assistance.
Research Focus
Key Achievements
Top Papers
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