Biomedical Image Processing

Research Areas

The research group of Professor Dr. Alexander Effland focuses on applied mathematics with a particular focus on Biomedical Imaging Processing and Modelling. We are interested in developing novel algorithms and methods with a strong emphasis on biomedical applications. Our research topics include mathematical image processing & computer vision, machine/deep learning, modeling, and calculus of variations, which are designed for diverse medical fields such as immunology, radiology, and cardiology.

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Mathematical Image Processing

In this research area, we target the development and advancement of techniques to tackle a variety of imaging problems such as image reconstruction or segmentation. Examples of these techniques include scale-space methods, primal-dual algorithms, variational methods, PDE-based methods, or higher-order regularization schemes, which might also be combined with recent learning-based methods in a mathematically rigorous manner.

Applied Computer Vision and Medical Imaging

The research group focuses on numerous challenges in (applied) computer vision including e.g., denoising, super-resolution, deblurring, colorization, and style transfer, for which machine learning methods are developed. Moreover, we are interested in medical applications emerging in diverse medical specialties such as (neuro-)radiology, immunology, dermatology, oncology, pathology, or ophthalmology.

Bayesian Uncertainty
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Shared Prior Learning
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Mathematical Foundations of Machine Learning

In this research area, mathematical methods are exploited to rigorously analyze machine learning techniques, which are designed for image processing applications or inverse problems.

Cardiac Modeling

In this research area, mathematical and AI-based techniques for quantitative problems emerging in cardiology are advanced to facilitate, for instance, patient-specific modeling, cardiac electrophysiology, or disease understanding.

Heart Reconstruction
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