Papers
11
Total Citations
259
H-Index
8
About
Mohammed Hossny is a leading researcher at the intersection of surgical robotics, computer vision, and autonomous systems. His work is defined by pioneering applications of deep learning to critical challenges in medical technology and human-robot interaction. Hossny’s most influential contribution is his 2017 work on surgical tool segmentation, which introduced a hybrid deep CNN-RNN autoencoder-decoder architecture. This framework, cited 79 times, is foundational for enabling context-aware surgical phase recognition and flow identification—a key step toward trusted autonomous surgical robots. He has also made significant strides in brain-computer interfacing, developing deep recurrent-convolutional neural networks for classifying steady-state visual evoked potentials (SSVEP) from EEG signals, a method that enhances the reliability of non-invasive BCI systems. Beyond the operating room, Hossny has advanced animal pose estimation with semantic body parts segmentation for quadrupedal locomotion analysis and applied RGB-D imaging for human body parts segmentation with attached props, impacting fields from biomechanics to safety. His work on ocular biomechanics and autonomous image fusion further demonstrates a commitment to bridging computational models with real-world robotic applications, establishing him as a versatile innovator in modern robotics.
Research Focus
Key Achievements
Top Papers
- 1Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder79 citations · 2017
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- 4Semantic body parts segmentation for quadrupedal animals30 citations · 2016
- 5Body Parts Segmentation with Attached Props Using RGB-D Imaging22 citations · 2015
- 6Safety applications using Kinect technology19 citations · 2014
- 7Towards autonomous image fusion18 citations · 2010
- 8Towards Trusted Autonomous Surgical Robots12 citations · 2018
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