🚀 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. hashtag#AI hashtag#MedicalImaging hashtag#XAI hashtag#Healthcare hashtag#BiomedicalEngineering hashtag#ArtificialIntelligence hashtag#MachineLearning