ENTRep Challenge and Workshop:
Advancing Vision-Language AI for
ENT Endoscopy Analysis

📢 We release the Public Dataset with Dataset Usage Guideline

📢 Public Test Submission Links for Tracks 1, Track 2, and Track 3 are now available on Codabench!

Challenge Overview: Step into the forefront of medical AI research with the ENTRep Challenge, organized as part of ACM MM 2025 in Dublin, Ireland. This initiative offers exclusive access to a high-quality, expert-annotated dataset of ENT endoscopy images collected at Thong Nhat Hospital in Ho Chi Minh City, Vietnam. Clinical specialists have meticulously labeled each image to capture various ENT pathologies and anatomical nuances. In this challenge, you will develop innovative solutions across three pivotal tasks:

• Image Classification: Develop algorithms that precisely classify ENT endoscopy images by anatomical region and pathology, paving the way for more accurate diagnoses and streamlined clinical workflows.

• Image-to-Image Retrieval: Design efficient systems that can swiftly retrieve visually similar images from an extensive database, enabling clinicians to conduct comparative analyses and make informed decisions.

• Text-to-Image Retrieval: Create advanced methods to bridge the gap between clinical narratives and visual data, by matching detailed text descriptions with their corresponding images to enhance data accessibility and diagnostic precision.

Register now and join us in shaping the future of ENT diagnostics at ACM MM 2025.

For questions, please contact the organizers at ntthuan@selab.hcmus.edu.vn.

We would like to invite submissions of both short and long papers. Short papers (non-archived) should be formatted in the ACM MM style and are limited to 4 pages, excluding references. Long papers (archived) must also follow the ACM MM format and can be up to 8 pages, excluding references. All submissions must adhere to the ACM MM submission policies. 

Topics for the workshop include, but are not limited to:

• Medical Large Language Models

• Information Retrieval

• Multi-Modal Learning

• Explainable AI in Medical Imaging

• Clinical Applications and Integration

• Domain Adaptation and Transfer Learning

• Synthetic Data Generation

• Automated Medical Report Generation

• AI for Rare Pathologies

• Privacy-Preserving AI

• Interactive and Clinician-AI Collaboration Systems

Submission website: https://cmt3.research.microsoft.com/ENTRep2025.

• Registration opened: 26/3/2025

• Public data released: 15/4/2025

• Registration closed: 31/5/2025

• Public session opened: 05/6/2025

• Public session closed: 22/6/2025

• Private data released: 23/6/2025

• Private session opened: 23/6/2025

• Private session closed: 30/6/2025

• Results, Report, Paper submission deadline: 10/7/2025

• Notification: 24/7/2025

• Camera-ready submission: 26/8/2025

• Challenge date: 31-27/10/2025