Modern imaging methods enable us to capture structure and dynamics in unprecedented detail and with high temporal and spatial resolution. The challenge is to make full use of the potential of such "big" imaging data to obtain quantitative results, test hypotheses, and develop new theories and models. Manual or semi-automated image analysis workflows quickly become a bottleneck because they do...
In medical imaging, dedicated cameras are used to capture images, and medical images are typically three-dimensional images. These three-dimensional images are generated from a series of datagrams or images of sections and/or projections, which is achieved by tomographic reconstruction. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET) and Single...
Bleaching and phototoxicity in light microscopy, or beam-induced sample damage in electron microscopy, are just some of the many reasons why exposure must be kept low in microscopy. However, limiting exposure can result in noisy image acquisition. These noisy image data are not only tedious to browse, but often make automated image processing difficult or even impossible. In this talk, I will...
I will talk about two separate research areas for tracking objects over time and instances. The first applies to the scenario of tracking an object over time, e.g. a known 3D rigid model. I will introduce Bayesian and particle filters and explain some technical ideas to make them fast and accurate. In the second part of the lecture I will introduce the field of tracking a large set of objects...
The course will introduce current machine learning models for image segmentation, including semantic- and instance segmentation. It will also cover how to apply such models to large data. A respective tutorial at the end of the seminar series will give hands-on experience in training and applying such models.
Join us in our journey through selected methods of telling the story of your dataset through visualizations. In the seminar, we will transform 3D segmentations into effectful Blender renderings.
Explainable machine learning involves two complementary types of questions: (1) Method designers and theorists ask: "Why do deep neural networks work so well? How can we further improve them? How can we provide formal performance guarantees?" (2) Instead, method users ask, "How did the network arrive at its conclusion? What variables determine the result, and in what way? Can the result be...
