This literature seminar explores seminal works applying deep learning techniques to scientific problems across disciplines such as biology, physics, and computer vision. The course is designed for advanced students with an interest in both the theory and application of deep learning. Each session is based on one or two key papers, with students responsible for preparing a four-page summary and delivering a 30-minute presentation, followed by an in-depth group discussion.
In this seminar the students will learn about recent methods in the field of deep learning for image analysis with a focus on biomedical images. Each week the students will read and understand 2 seminal research papers of a specific subtopic and present and discuss them within the group. The seminar will cover a range of topics including image classification, object detection and segmentation, object tracking, and generative modeling. Additionally, we will cover fundamental DL model architectures such as CNNs, Transformers, and Diffusion model