Papers
3
Total Citations
22
H-Index
2
About
Mansoor Hayat is a rising researcher at the forefront of computational medical imaging, with a focused expertise in enhancing the visual quality and analytical precision of stereo-endoscopic systems used in minimally invasive and robotic surgery. His primary contributions lie in the development of deep learning architectures that simultaneously address two critical challenges: super-resolution and surgical instrument segmentation. Hayat’s flagship work, SEGSRNet, introduces a novel framework that upscales low-resolution stereo endoscopic images while accurately identifying surgical tools, directly tackling the common problem of noise and blur that obscures fine anatomical details. This work has garnered 17 citations, signaling its immediate impact on the field. Building on this, his latest model, HAMSRNet, employs a hybrid attention multiscale mechanism to further refine image clarity, ensuring surgeons have reliable depth cues and visual feedback during complex procedures. By integrating state-of-the-art super-resolution techniques with robust segmentation, Hayat’s research is paving the way for safer, more precise surgical interventions, establishing him as a key innovator in the intersection of computer vision and medical robotics.
Research Focus
Key Achievements
Top Papers
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