Decision making algorithms are used in a multitude of different applications. Conventional
approaches for designing decision algorithms employ principled and simplified modelling …

Medical ultrasound imaging relies heavily on high-quality signal processing to provide
reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms …

In mechanical anomaly detection, algorithms with higher accuracy, such as those based on
artificial neural networks, are frequently constructed as black boxes, resulting in opaque …

In recent years, there have been significant advances in the use of deep learning methods in
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …

Deep algorithm unrolling has emerged as a powerful, model-based approach to developing
deep architectures that combine the interpretability of iterative algorithms with the …

Abstract Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for non-
invasive, dynamic visualization in medical diagnostics, yet Ultrasound Localization …

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on
the precise localization of contrast agent particles across a series of beamformed frames …

Model-based inversion played a dominant role in biomedical imaging prior to deep learning
gaining widespread popularity and broad recognition. Model-based techniques rely on a …

Much of the information needed for diagnosis and treatment monitoring of diseases like
cancer and cardiovascular disease is found at scales below the resolution limit of classic …

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on
the precise localization of contrast agent particles across consecutive beamformed frames …