Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …

With the massive proliferation of data-driven algorithms, such as deep learning-based
approaches, the availability of high-quality data is of great interest. Volumetric data is very …

Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms
in medical imaging are predominantly diverging from computer vision, where voxel grids …

Background In deep learning the most significant breakthrough in the field of image
recognition, object detection language processing was done by Convolutional Neural …

Feature selection (FS) may improve the performance, cost-efficiency, and understandability
of supervised machine learning models. In this paper, FS for the recently introduced …

Long-term field monitoring of leaf pigment content is informative for understanding plant
responses to environments distinct from regulated chambers but is impractical by …

In this article, we present a multicenter aortic vessel tree database collection, containing 56
aortas and their branches. The datasets have been acquired with computed tomography …

Data has become the most valuable resource in today's world. With the massive proliferation
of data-driven algorithms, such as deep learning-based approaches, the availability of data …

XAI refers to the techniques and methods for building AI applications which assist end users
to interpret output and predictions of AI models. Black box AI applications in high-stakes …

Deep neural networks are commonly used for medical purposes such as image generation,
segmentation, or classification. Besides this, they are often criticized as black boxes as their …