This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

Microplastic research is a rapidly developing field, with urgent needs for high throughput
and automated analysis techniques. We conducted a review covering image analysis from …

Recent research in artificial intelligence and machine learning has largely emphasized
general-purpose learning and ever-larger training sets and more and more compute. In …

In this paper, we present a new deep learning framework for 3-D tomographic
reconstruction. To this end, we map filtered back-projection-type algorithms to neural …

We describe an approach for incorporating prior knowledge into machine learning
algorithms. We aim at applications in physics and signal processing in which we know that …

Foundation large language models (LLMs) have shown an impressive ability to solve tasks
across a wide range of fields including health. To effectively solve personalized health tasks …

Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They
are used to aggregate activations of spatial locations to produce a fixed-size vector in …

Computer systems for medical diagnosis based on machine learning are not mere science
fiction. Despite undisputed potential benefits, such systems may also raise problems. Two …

Phase retrieval approaches based on deep learning (DL) provide a framework to obtain
phase information from an intensity hologram or diffraction pattern in a robust manner and in …

In computed tomography, image reconstruction from an insufficient angular range of
projection data is called limited angle tomography. Due to missing data, reconstructed …