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Explore Explainable AI (XAI) in Biomedical Imaging!
🚀 Explore Explainable AI (XAI) in Biomedical Imaging!
Join us for an in-depth tutorial on Explainable Artificial Intelligence (XAI) in Biomedical Imaging, where we will dive into cutting-edge methods for making AI-driven medical imaging more interpretable, transparent, and clinically useful.
🧠 What to Expect:
📌 Lecture & Hands-on sessions covering both visual and non-visual XAI techniques
📌 Insights into XAI robustness, evaluation, and clinical adoption
📌 Practical coding exercises using Python and tools like CAM, LIME, SHAP, TCAV, and UMAP
💡 Who should attend?
Researchers, clinicians, and AI enthusiasts eager to bridge the gap between AI models and real-world medical applications.
⏳ Duration: 2 hours
📍 Topics & Speakers:
🔹 Tutorial Part I (Lecture – 25 min)
📣 “Introduction to XAI in Biomedical Imaging”
👤 Nikolaos Karakatsanis – Assistant Professor of Biomedical Engineering, Weill Cornell Medical College, Cornell University
🔹 Tutorial Part II (Hands-on – 25 min)
📣 “Visual-based XAI Methods for Biomedical Imaging”
👤 Theofanis Ganitidis – Ph.D. Candidate, National Technical University of Athens
🔹 Tutorial Part III (Hands-on – 25 min)
📣 “Non-visual Based XAI Methods for Biomedical Imaging”
👤 Dimitris Fotopoulos – Ph.D. Candidate, Aristotle University of Thessaloniki
🔹 Tutorial Part IV (Lecture – 25 min)
📣 “Robustness of XAI in Biomedical Imaging”
👤 Kalliopi Dalakleidi – Post-doctoral Researcher, National Technical University of Athens
🔹 Tutorial Part V (Lecture – 10 min)
📣 “Evaluation of XAI by Clinicians – Challenges & Acceptance”
👤 Ioanna Chouvarda – Associate Professor, Aristotle University of Thessaloniki
You can pre-register here: https://authgr.zoom.us/meeting/register/DvG4ToJhQQyNHeGc-OXyFQ
Join us as we explore how XAI can improve trust, interpretability, and adoption of AI in healthcare.
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