Se Woon Cho
Papers
2
Total Citations
52
H-Index
2
About
Se Woon Cho is a researcher advancing the intersection of deep learning and medical robotics, with a primary focus on semantic segmentation and image enhancement for surgical and autonomous systems. His most notable contribution, the Dual-Stream Residual Dense Network (DSRD-Net), addresses the critical challenge of instrument segmentation in robot-assisted minimally invasive surgery (RMIS). This work, cited 37 times, tackles real-world surgical complexities such as specular reflection, blood, fogging, and cluttered backgrounds, directly improving safety by reducing human error and tissue damage risk. Cho also developed a Modified Perceptual Cycle Generative Adversarial Network for low-light image enhancement, achieving 15 citations by boosting segmentation accuracy in challenging visual conditions—a technique applicable to both autonomous vehicles and AI-driven robotics. His research demonstrates a commitment to robust, real-time visual perception in high-stakes environments, blending architectural innovation with practical deployment considerations. Through these contributions, Cho is helping to make AI-assisted surgery and autonomous navigation more reliable under adverse conditions, marking him as a rising figure in applied computer vision and robotic perception.
Research Focus
Key Achievements
Top Papers
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- 2