Papers
3
Total Citations
14
H-Index
2
About
Chen-Chen Fan is a rising researcher at the intersection of medical robotics and artificial intelligence, whose work focuses on enhancing robot-assisted minimally invasive surgery (RMIS) and neurological diagnostics. Fan’s primary contributions lie in developing real-time, computationally efficient frameworks for surgical guidance and disease diagnosis. Their most cited work introduces a multi-task framework for simultaneous guidewire segmentation and endpoint localization in endovascular interventions—a critical step toward reducing radiation exposure and procedure time. Another key contribution involves a group feature learning and domain adversarial neural network for diagnosing amnestic mild cognitive impairment (aMCI) from EEG signals, offering an objective, automated tool to help prevent Alzheimer’s disease. Most recently, Fan proposed a dual-stream architecture for real-time morphological analysis of aneurysms during RMIS, tackling challenges like ambiguous boundaries and obscured surfaces. With over 14 citations across their top papers, Fan’s research demonstrates growing impact in surgical robotics and medical AI. Their work is particularly notable for addressing real-world constraints—such as limited computational resources and complex anatomical structures—making their solutions practical for clinical deployment.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3