Nahid Nazifi
Papers
1
Total Citations
3
H-Index
1
About
Nahid Nazifi is a rising researcher at the intersection of medical imaging and deep learning, with a primary focus on advancing Wireless Capsule Endoscopy (WCE) technology. Her work addresses a critical challenge in gastrointestinal diagnostics: enabling autonomous, controlled navigation of endoscopic capsules through the digestive tract. In her most-cited paper, "Self-supervised monocular pose and depth estimation for wireless capsule endoscopy using transformers" (2024), Nazifi introduces a novel transformer-based architecture that learns depth and camera motion from unlabeled video sequences. This self-supervised approach eliminates the need for expensive ground-truth data, making it highly scalable for real-world clinical deployment. By estimating 3D scene geometry from monocular images, her method lays the groundwork for precise capsule localization and obstacle avoidance—key requirements for active capsule control. Though early in her career, her work has already garnered 3 citations, signaling growing interest from the biomedical engineering and computer vision communities. Nazifi’s contributions are particularly notable for bridging state-of-the-art transformer models with the constraints of low-power, miniaturized medical devices, offering a promising path toward fully autonomous WCE systems that could revolutionize non-invasive gastrointestinal screening.
Research Focus
Key Achievements
Top Papers
- 1