CV4Clinical 2026: Computer Vision for Real-world Clinical Translation

About The Workshop

The CV4Clinical 2026 workshop aims to bridge advanced computer vision techniques and the complex realities of clinical applications. While CVPR continues to drive progress in imaging, understanding and action, this workshop explores how such advances can translate into real-world medical impacts. By convening experts from academia, industry, and healthcare, CV4Clinical 2026 will focus on key issues of model effectiveness, reliability, and reproducibility in clinical deployment; critically examine the opportunities and challenges of emerging techniques such as foundation and generative models; and explore future directions for socially meaningful and clinically grounded medical AI.

Call for Papers

We welcome submissions of long papers (4-8 pages, excluding references) for oral or poster presentation at the workshop. Accepted papers will be published in the CVPR 2026 Workshop Proceedings.

📌 Topics of Interest

CV4Clinical 2026 focuses on bridging computer vision with real-world clinical translation. The workshop welcomes contributions in, but not limited to, the following areas:

🎯 Lesion and Tumor Understanding: Robust segmentation under heterogeneous conditions, multi-rater variability modeling, adaptive delineation
⏱️ Temporal and Longitudinal Analysis: Disease progression modeling, temporal imaging data learning, short- to long-term outcome analysis
🤝 Human-AI Collaboration: Interactive and trustworthy systems, human-in-the-loop learning, explainability, interfaces, clinical workflow integration
📊 Benchmarking, and Deployment: Clinical evaluation protocols, domain adaptation, robustness, deployment case studies, regulatory readiness
📝 Generative Modeling: Data synthesis, modality translation, multimodal clinical data, cross-modal alignments, digital twins, and personalization
💬 Vision & Language in Medicine: Medical reports in clinical flows, LLM-based medical applications, hallucination and interpretability
🔥 Foundation Models in Medicine: SAM/DINO-based applications, world model state transfer, continual learning, and active learning
📐 Medical 3D Learning: 3D understanding, real-time motion estimation and tracking, population-level statistical shape modelling

By combining these themes, CV4Clinical 2026 aims to foster interdisciplinary dialogue and identify research directions that will shape the future of clinically reliable, socially meaningful, and ethically grounded medical AI.

Review & Selection: All submissions will be evaluated by the program committee based on relevance and quality. Oral and poster presentations will be selected accordingly.