Automated analysis of endoscopic imagery is a critical yet underdeveloped component of ENT (ear, nose, and throat) care, hindered by variability in devices and operators, subtle and localized findings, and fine-grained distinctions such as laterality and vocal-fold state. In addition to classification, clinicians require reliable retrieval of similar cases, both visually and through concise textual descriptions. These capabilities are rarely supported by existing public benchmarks. To this end, we introduce ENTRep, the ACM Multimedia 2025 Grand Challenge on ENT endoscopy analysis, which integrates fine-grained anatomical classification with image-to-image and text-to-image retrieval under bilingual (Vietnamese and English) clinical supervision. Specifically, the dataset comprises expert-annotated images, labeled for anatomical region and normal or abnormal status, and accompanied by dual-language narrative descriptions. In addition, we define three benchmark tasks, standardize the submission protocol, and evaluate performance on public and private test splits using server-side scoring. Moreover, we report results from the top-performing teams and provide an insight discussion.
@article{arxiv.2508.04801,
title = {ACM Multimedia Grand Challenge on ENT Endoscopy Analysis},
author = {Trong-Thuan Nguyen and Viet-Tham Huynh and Thao Thi Phuong Dao and Ha Nguyen Thi and Tien To Vu Thuy and Uyen Hanh Tran and Tam V. Nguyen and Thanh Dinh Le and Minh-Triet Tran},
journal= {arXiv preprint arXiv:2508.04801},
year = {2025}
}