Anas Tharek

Papers

Total Citations

H-Index

About

Anas Tharek is a rising researcher in the field of surgical computer vision, with a focused interest in applying deep learning to improve minimally invasive procedures. His primary research area centers on the real-time segmentation of anatomical structures during laparoscopic surgery, particularly the liver and gallbladder. In his most cited work, "Real-time robust liver and gallbladder segmentation during laparoscopic cholecystectomy using convolutional neural networks: an analysis," Tharek tackles a critical challenge in the field: the inconsistent performance of neural network models caused by variations in camera hardware, settings, and institutional annotation protocols across different surgical datasets. By systematically analyzing these factors, his research aims to identify the optimal conditions for robust, generalizable segmentation models. Although his career is in its early stages, with this key paper accumulating 3 citations, Tharek’s work is directly relevant to enhancing surgical safety and efficiency. His contributions are foundational for developing more reliable AI-assisted tools that can adapt to real-world clinical variability, marking him as a promising voice in the integration of artificial intelligence into operative practice.