https://studiegids.vu.nl/en/courses/2024-2025/XM_0151The student can explain the principles and use of neural computing via feed-forward neural networks and, particularly, convolutional neural networks.The student can apply deep learning architectures to medical images.The student can analyse the performance of different deep learning architectures in the context of a deep learning challenge.The student can use their insight in technical aspects of medical imaging to construct networks that solve specific technical aspects in the imaging pipeline.The student can justify the use of different physics aspects in deep learning.The student can implement and train a fully connected neural network using NumPy.Artificial Intelligence has proven to be a great tool for helping radiologists and pathologists in diagnosing patients, and, ultimately, selecting the best possible patient-specific treatment. Computers can analyse digital images at an unmet speed and can detect patterns that are missed by medical experts. The development and application of artificial intelligence in medical imaging has sped up due to (1) widely available digital medical images, (2) freely available machine learning tools, and (3) high computing performance and GPU’s in particular. This combination has led to applications where computers are highly accurate in detecting patterns, and lesions to support diagnosis and prognosis. AI is used over the entire front of medical imaging, including designing optimal image acquisition schemes, acceleration of imaging, reconstruction of imaging, image enhancement, segmentation and classification. In this course, we will focus on applying deep learning for digital medical image acquisition, processing and automatic analysis of images. The course will introduce the basic concepts of deep learning. Students will get hands-on experience in using the most common deep neural networks that are used in medical imaging, including convolutional neural networks such as U-net. Ultimately, the core of the course will focus on combining your technical background and knowledge about physics to allow you to apply and enhance deep learning approaches for medical imaging.LectureComputer lab session/practical trainingPresentation/symposiumSelf-study This course contains some self-study via Canvas, such that the students come well-prepared to the classes. The classes will be interactive and activating. We have 3 blocks, each block contains 1 week of predominantly theory and 1-2 weeks of hands-on programming of neural networks. The final exercise will be presented as poster to your peers.The course consists of 3 blocks, each with an assignment.My first network (35%)CNN (25%)Image reconstruction (20%)At the end of the course, a mini-symposium will be held in which the students present the results of Block 3 to each other. The posters and discussion will be evaluated by the TAs and teachers.Poster Presentation (20%):"Understanding Deep Learning" by Prince --> free download at www.udlbook.github.ioSyllabus:On CanvasPractical training material:Will be handed out during the classesSoftware:Python and PytorchRecommended: Students from the BMTP master Students from the Physics master any other student that fulfills the prior knowledge This course is not meant for AI MSc students as it overlaps with their core courses on explaining the basics of deep learning. There is a separate "AI for medical imaging" course for them.Participation in the lectures and practicals is expected; only 1 out of 7 classes and practicals can be missed. Contact the coördinator if you are missing a class. Participants must prepare the classes via self study.The students are proficient in programming in Python. The students should have an understanding of calculus (derivatives, chain rule, etc.) The students should have a basic understanding of medical imaging. No prior experience in machine learning is expected/required.We are using Canvas