Introduction to Medical Image Analysis Using Convolutional Neural Networks

Level: Introductory Length: 7 hours Format: In-Person Lecture Intended Audience: Students, researchers, and engineers from academia and industry, who seek to obtain first practical working knowledge in deep learning. Description: Segmentation, detection, and classification are major tasks in medical image analysis and image understanding. Medical imaging researchers heavily use the results of recent developments in machine learning approaches, and with deep learning methods they achieve significantly better results in many real-world problems compared to previous solutions. The course aims to enable students and professionals to apply deep learning methods to their data and problem. Using an interactive programming environment, participants of the course will explore all required steps in practice and learn tools and techniques from data preparation to result interpretation. We will work on example data and train models to segment anatomical structures, to detect abnormalities, and to classify them. Simple methods to explain predictions and assess network uncertainty will be discussed briefly as well. Participants will work in a prepared online environment providing selected deep learning toolkit installations, example data, and fully functional skeleton code as a basis for own experiments. Learning Outcomes: This course will enable you to: - get an overview of state of the art deep learning methods in medical applications - construct computing pipeline using Python based infrastructure, using frameworks (Keras, Tensorflow) commonly used for research - select a suitable deep learning network architecture for a given problem and implement it - explain and interpret learning progress using appropriate metrics - interpret the resulting model performance using simple visual analytics Instructor(s): Markus Thorsten Wenzel works on machine learning methods for medical applications since 2005 and has published more than 30 conference and journal papers on the subject. He received his PhD for his work on decision support systems for breast care. As key scientist for cognitive computing at Fraunhofer MEVIS, he is an experienced teacher and lecturer for academia and continuing education for participants from industry and research. He has acquired and led several international research projects as well as industry cooperations. Hans Meine is a senior scientist who has been using machine learning for image analysis since 2002, and focused on various medical applications at Fraunhofer MEVIS since 2011. Since early 2016, he is organizing the internal training and coaching of Fraunhofer MEVIS staff for the new methodologies in Deep Learning. At MEVIS, he takes responsibility for the necessary infrastructure and strategic developments for collaborative research and development of AI algorithms. His team scored top positions using Deep Learning in a few challenges, such as the “Liver and Tumor Segmentation” challenges at ISBI and MICCAI 2017. Event: SPIE Medical Imaging 2023 Course Held: 19 February 2023