Suzan Üsküdarlı

Papers

Total Citations

101

H-Index

About

Suzan Üsküdarlı is a leading figure in information retrieval and multimedia analysis, with a particular focus on medical image processing and cross-language evaluation. Her most impactful work stems from her deep involvement with the ImageCLEF initiative, where she has significantly advanced the benchmarking of image retrieval systems. Her highly cited paper, "ImageCLEF 2014: Overview and Analysis of the Results" (101 citations), provides a comprehensive analysis of state-of-the-art techniques in medical image annotation and retrieval, establishing critical baselines for the field. Üsküdarlı’s contributions extend to developing robust methods for concept detection and multimodal information extraction, enabling more effective search across visual and textual data. Her work has shaped how researchers evaluate and improve systems for tasks like organ and disease identification in radiology images. Through her leadership in organizing international evaluation campaigns and her rigorous analytical frameworks, Üsküdarlı has left a lasting impact on the reproducibility and progress of multimedia retrieval research, making her a key reference for students and scholars working at the intersection of computer vision and information science.