AI for Medical Imaging

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AI for Medical Imaging

Recent advances in AI have led to major breakthroughs in many scientific fields, including medical imaging. Much of the current success can be attributed to the development of powerful deep learning methods that learn from massive data. However, many mysteries of deep learning remain to be unravelled, for example, what is the appropriate inductive bias in specific applications, what leads to vulnerability at test time, and what causes AI uncertainty from a Bayesian point of view.

Delft AI Lab

We are a TU Delft AI lab that strives to study such interesting problems, in the domain of medical imaging. We are driven by our curiosity to understand AI and enthusiasm to improve AI for medical imaging applications. Besides, we work on clinical utilization of AI, for various applications such as image quantification, clinical diagnosis, and risk prediction.

Research Projects

We have a wide range of research projects open for students at bachelor, master, and PhD level, in both technical and clinical flavors. Examples include generalizability of deep learning in medical image analysis, data visualization, risk stratification, image segmentation and registration.

We welcome students who are enthusiastic in AI and medical imaging to join us. For more information, please contact Q.Tao@tudelft.nl.

Qian Tao

Biography

Qian Tao received her BSc degree (with Distinction) in Electrical Engineering, and her MSc degree (with Distinction) in Biomedical Engineering, both from Fudan University, Shanghai, China. She received her PhD degree from University of Twente, the Netherlands, with her PhD thesis entitled "Face Verification for Mobile Personal Devices", which presented a first-generation biometric authentication system on a mobile device. Her Ph.D work contributed to the 3Dface project of European Commission for the prototype biometric passport. Since 2009 Qian Tao has been working at the Division of Image Processing, Department of Radiology, Leiden University Medical Center, and her multidisciplinary research lines included cardiac MRI analysis and clinical applications, image-guided treatment of cardiac arrhythmia, and artificial intelligence in Radiology. Her research interest includes medical image analysis, machine learning, and their state-of-the-art integration in theory and in practice.

Publications

Courses 2022

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