Seminar: Artificial Intelligence in Radiology: Applications and Research
Image created with assistance from DALL-E, utilizing design elements provided by OpenAI ChatGPT, and finalized by Shadi Albarqouni
Seminar (2 SWS): Artificial Intelligence in Radiology: Applications and Research (Artifizielle Intelligenz in der Radiologie: Anwendungen und Forschung)
Lecturers: Prof. Dr. Shadi Albarqouni
Tutors: Dr. Elodie Germani, David Gaviria, Yiheneg Xiong
Time: November 15 – Jan 31 (every Friday, 11:30 AM - 1:00 PM 16:00 - 17:30)
Format: Online (Zoom Meeting) with potential face-to-face meetings, if participants interested!
Language: English
Description
The Computational Imaging Research (Albarqouni Lab) at the Clinic of Diagnostic and Interventional Radiology at the University Hospital Bonn is pleased to present a seminar on Artificial Intelligence in Radiology: Applications and Research. This cutting-edge seminar, spanning two semester hours per week (2 SWS), is specially curated for medical students to explore the transformative role of deep learning in medical imaging, with a focus on tasks such as classification, detection, segmentation, reconstruction, tracking, and disease progression.
Course Objectives
Upon completing this seminar, students will be able to:
- Grasp deep learning concepts and their specific applications in medical imaging.
- Analyze advanced image analysis tasks, including classification, detection, segmentation, and disease progression modeling.
- Evaluate ethical and regulatory considerations in the deployment of AI within healthcare.
- Discuss the future potential of AI-driven solutions in personalized medicine and patient care.
Target Audience
This seminar is designed for medical students at the Faculty of Medicine, University Hospital Bonn, who are keen on leveraging AI technologies to enhance their medical practice, specifically within diagnostic imaging.
Prerequisites and Desired Knowledge
- Prerequisites: Familiarity with fundamental medical imaging techniques and an interest in AI applications.
- Desired: Understanding of medical diagnostics and clinical data interpretation.
Seminar Topics and Schedule (Tentative)
| Date | Topic | Description | Materials |
|---|---|---|---|
| Nov 15 | Introduction to Medical Imaging | Overview of imaging modalities (X-ray, MRI, CT, ultrasound) and their role in diagnostics. | |
| Nov 22 | Deep Learning Foundations | Introduction to deep learning concepts specific to medical imaging. | |
| Nov 29 | Classification in Medical Imaging | Exploring classification techniques in diagnosing diseases from medical images. | |
| Dec 6 | Detection and Segmentation | Deep learning methods for detecting and segmenting key structures in medical images. | |
| Dec 13 | Reconstruction Techniques | Using deep learning to reconstruct high-quality images from raw data, including noise reduction. | |
| Dec 20 | Tracking and Disease Progression | Applying AI to track anatomical changes and model disease progression over time. | |
| Jan 10 | Advanced Topics | Advanced AI techniques for personalized medicine. | |
| Jan 17 | Student Paper Presentations (1) | Students present research papers on deep learning in medical imaging. | |
| Jan 24 | Student Paper Presentations (2) | Continuation of student presentations. | |
| Jan 31 | Course Wrap-up and Future Perspectives | Summary, feedback, and discussion on future AI applications in medical practice. |
Learning Format
One of the seminar’s unique teaching concepts is “Teach It to Yourself.” Participants (limited to 20 partisipants) will present selected chapter and papers, engage in discussions, and critically analyze case studies on deep learning applications in medical imaging.
Registration (closed)
Interested in this seminar, please register via email to Dr. Elodie Germani and Prof. Dr. Shadi Albarqouni
Elodie Germani
Postdoctoral Researcher
Elodie Germani works with Prof. Shadi Albarqouni as a postdoctoral researcher. She did her PhD at the University of Rennes, under the supervision of Dr. Camille Maumet and Prof. Elisa Fromont. After four years of medicine school at the University of Versailles, she took a shift in her career and started a Master’s degree in bioinformatics. Her research focuses on exploring, modelling and building solutions to take into account the variability of data in medical imaging, particularly using deep representation learning. During her PhD, her goal was to facilitate the re-use of data shared on public databases by taking into account the different sources of variability. In the future, she would like to focus more in the use of real-world data and on the robustness of machine learning models to dataset shifts and privacy attacks.
