Advanced Computational Methods for Biomedical Imaging. The field of imaging science is undergoing rapid growth. Biomedical imaging and its analysis play a fundamental role in comprehending, visualizing, and quantifying medical images for various clinical applications. Computational image analysis techniques can significantly improve disease diagnosis by making it faster, more effective, and fairer. This seminar course will focus on understanding different imaging tasks associated with medicine and applications of advanced computational methods (such as computer vision, deep learning) to solve important biomedical imaging problems. The course will also examine some key topics and advanced techniques in computer vision and medical imaging, reading, reviewing, presenting, and discussing papers published in major computer vision and medical imaging venues (e.g., CVPR, IEEE TMI, MedIA, MICCAI, ICCV, etc.). The course will be taught with a good mix of theories and applications with different case studies. Topics include basics of computer vision and medical imaging, different image processing techniques, machine learning and deep learning, important image databases, ML/DL and medical imaging tools, generative methods, self-supervised learning, federated learning, and fairness and explainability. Expected prior knowledge and skills include programming ability at an intermediate level, familiarity with probability and statistics, knowledge of Python (highly recommended), machine learning (460G or similar), or approval of the instructor.
Advanced topics in computer graphics, computer vision, and multimedia systems. Specific topics include but are not limited to: isophotes, volume rendering, displacement mapping, geographic information systems (GIS), remote sensing topics, large scale sensor networks, video and audio encoding, visualization, immersive environments, and multimedia interfaces. May be repeated to a maximum of up to 6 credit hours, with no more than 3 in the same topic.
Advanced topics in computer graphics, computer vision, and multimedia systems. Specific topics include but are not limited to: isophotes, volume rendering, displacement mapping, geographic information systems (GIS), remote sensing topics, large scale sensor networks, video and audio encoding, visualization, immersive environments, and multimedia interfaces. May be repeated to a maximum of up to 6 credit hours, with no more than 3 in the same topic.
