Chiara Lena
Papers
1
Total Citations
4
H-Index
1
About
Chiara Lena is a rising researcher at the intersection of computer vision and robotic-assisted surgery. Her work focuses on adapting large-scale foundation models to the unique challenges of the operating room, particularly for depth estimation and 3D reconstruction in endoscopic procedures. In her highly cited 2025 paper, “DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation Model,” Lena introduced an innovative parameter-efficient fine-tuning method that allows powerful general-purpose depth models to perform accurately on limited surgical data without catastrophic forgetting. This approach addresses a critical bottleneck in surgical AI: the scarcity of annotated medical images. With 4 citations already in its first year, DARES is gaining traction as a practical solution for real-time 3D visualization in robotic surgery. Lena’s work exemplifies how self-supervised learning and foundation model adaptation can bridge the gap between general computer vision and specialized clinical applications. Her contributions are paving the way for safer, more precise minimally invasive procedures, making her a promising voice in the next generation of surgical AI research.
Research Focus
Key Achievements
Top Papers
- 1