CVAMD 2025: 3rd Workshop on Computer Vision for Automated Medical Diagnosis

About The Workshop

The striding advances of computer vision techniques are revolutionizing many long-standing automatic medical diagnosis tasks. Emerging trends—such as Large Language Models (LLMs), Foundation Models (FMs), advanced learning paradigms (e.g., un-/semi-/self-supervised learning), and considerations of fairness and generalization—remain underexplored for secure and reliable automated medical diagnosis. Distinctly, this workshop emphasizes integrating insights from clinicians and radiologists alongside technical discussions to better advance the field.

Call for Papers

We welcome three types of submissions for oral or poster presentation at the workshop:

📄 Long Papers – Up to 8 pages (excluding references). Accepted long papers will be published in the ICCV 2025 Workshop Proceedings.
📝 Extended Abstracts – Up to 4 pages (excluding references), non-archived. Great for sharing work-in-progress or previously published work.
🌟 Highlights – A concise 1-page summary, non-archived. Perfect for showcasing recently accepted or notable papers and boosting visibility and engagement.

🧠 Topics of Interest

We invite contributions on the following topics (but not limited to):

🔥 Foundation Models (FMs) and Large Language Models (LLMs) in healthcare
🤖 AI Agents for medical decision-making and workflow automation
🧾 Interpretable & trustworthy AI for medical diagnosis
📊 Predicting clinical outcomes from medical image analysis
🔍 Multimodal biomedical image analysis
⚖️ Fairness, robustness, and generalization in medical computer vision
🧠 Embedding medical knowledge in vision systems
🧾 Generating diagnostic reports from medical images
🧭 Clinical reasoning-aware vision system design
🧹 Learning robust representations from noisy annotations
🩺 Advances in disease diagnosis and management with computer vision
🧬 Medical anomaly and out-of-distribution prediction
🖼️ Medical image registration and classification (MRI/CT/PET)
📌 Organ and lesion segmentation/detection
📈 Longitudinal studies with computer vision
♾️ Life-long and active learning in medical vision

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

📚 Note: Accepted long papers will be published alongside the ICCV 2025 proceedings.

📢 Submission Guidelines

📘 Long Paper Track (4-8 pages, with Proceedings)

Submit your original research (4–8 pages, excluding references) in the ICCV 2025 format. Accepted papers will be published in the official ICCV workshop proceedings.
Please ensure your submission adheres to the ICCV 2025 Dual Submission Policy. Work must be sufficiently original and not under review elsewhere.
📝 Extended Abstracts (2–4 pages, non-archived)

Ideal for work-in-progress or previously published studies relevant to the workshop. Submissions must be 2–4 pages (excluding references).
These abstracts will not be included in the official proceedings, making them suitable for showcasing ongoing work or gaining community feedback. Please verify the double submission policy of your target venue if you plan to submit elsewhere later.
🌟 Highlights Track (1-page summary + external link)

Showcase your recently accepted or notable work (e.g., from NeurIPS, ICLR, CVPR). Simply provide a concise 1-page summary with a link to the full paper.
This track is lightly curated—no full review process—and is a great opportunity to increase visibility and spark discussion.

All papers can be submitted through OpenReview.

🏆 Awards

We are excited to present the following honors at the workshop:

🥇 Best Paper Award – One winner per track
🎓 Best Student Paper Award – Recognizing exceptional work led by a student author, one per track
🖼️ Best Poster Award – Honoring the most impactful poster presentation in each track

In addition, each track will feature a selection of oral and poster presentations. The number of slots for each format will be finalized and announced following the review process.