Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Diffusion models have emerged as the new state-of-the-art generative model with high
quality samples, with intriguing properties such as mode coverage and high flexibility. They …

In order to reduce the potential radiation risk, low-dose CT has attracted an increasing
attention. However, simply lowering the radiation dose will significantly degrade the image …

The combination of tomographic imaging and deep learning, or machine learning in
general, promises to empower not only image analysis but also image reconstruction. The …

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in
combination with other methods. The planning and delivery of radiotherapy treatment is a …

Recovering a function or high-dimensional parameter vector from indirect measurements is
a central task in various scientific areas. Several methods for solving such inverse problems …

Traditional imaging systems exhibit a well-known trade-off between the resolution and the
field of view of their captured images. Typical cameras and microscopes can either “zoom in” …

The development of fast and accurate image reconstruction algorithms is a central aspect of
computed tomography. In this paper, we investigate this issue for the sparse data problem in …

Topological semimetals are well known for the linear energy band dispersion in the bulk
state and topologically protected surface state with arc-like Fermi surface. The angle …