Improved Mass Calibration in MALDI MSI Using Neural Network-Based Recalibration.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful imaging method for generating molecular maps of biological samples and has numerous applications in biomedical research. A key challenge in MALDI MSI is to reliably map observed mass peaks to theoretical masses, which can be difficult due to mass shifts that occur during the measurement process. In this paper, we propose MassShiftNet, a novel self-supervised learning framework for mass recalibration.

Conditional Invertible Neural Networks for Medical Imaging.

Over recent years, deep learning methods have become an increasingly popular choice for solving tasks from the field of inverse problems. Many of these new data-driven methods have produced impressive results, although most only give point estimates for the reconstruction. However, especially in the analysis of ill-posed inverse problems, the study of uncertainties is essential.

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint.

Stronger shared taste for natural aesthetic domains than for artifacts of human culture.

Individuals can be aesthetically engaged by a diverse array of visual experiences (paintings, mountain vistas, etc.), yet the processes that support this fundamental mode of interaction with the world are poorly understood. We tested whether there are systematic differences in the degree of shared taste across visual aesthetic domains.