Daniel Haro-Mendoza
Papers
2
Total Citations
4
H-Index
2
About
Daniel Haro-Mendoza is a rising researcher in the rapidly evolving field of computer-assisted surgery, with a primary focus on advanced medical image segmentation. His work directly addresses critical challenges in laparoscopic and robot-assisted procedures, aiming to enhance surgical precision, safety, and real-time guidance. Haro-Mendoza's key contributions lie in developing novel segmentation techniques for both surgical instruments and organs. His most cited work, "Advanced Dual-Branch U-Net Decoder for Precise and Robust Surgical Instrument and Organ Segmentation" (2025), introduces a sophisticated neural network architecture designed to overcome the significant hurdles posed by noisy and complex surgical environments. Complementing this supervised approach, his paper "UMInSe: An Unsupervised Method for Segmentation and Detection of Surgical Instruments based on K-means" (2024) tackles the critical bottleneck of costly manual annotations by proposing a fully unsupervised segmentation pipeline. This innovative method offers a promising, scalable solution for instrument detection without the need for labeled data. With early citations already accumulating on these foundational works, Haro-Mendoza is establishing himself as a key contributor to making autonomous surgical guidance more robust and accessible.
Research Focus
Key Achievements
Top Papers
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