Zobrazeno 1 - 10
of 7 399
pro vyhledávání: '"A. Berens"'
Autor:
Müller, Sarah, Fay, Louisa, Koch, Lisa M., Gatidis, Sergios, Küstner, Thomas, Berens, Philipp
Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more. As a result, deep learning models tend to learn spurious correlations instead of causally related features, limit
Externí odkaz:
http://arxiv.org/abs/2407.18792
The presence of tumor hypoxia is known to correlate with poor patient prognosis. Measurement of tissue oxygen concentration can be challenging, but recent advancements using positron annihilation lifetime spectroscopy (PALS) in three-dimensional posi
Externí odkaz:
http://arxiv.org/abs/2407.03573
Interpretability is a key requirement for the use of machine learning models in high-stakes applications, including medical diagnosis. Explaining black-box models mostly relies on post-hoc methods that do not faithfully reflect the model's behavior.
Externí odkaz:
http://arxiv.org/abs/2406.15168
Retinal blood vessel segmentation can extract clinically relevant information from fundus images. As manual tracing is cumbersome, algorithms based on Convolution Neural Networks have been developed. Such studies have used small publicly available da
Externí odkaz:
http://arxiv.org/abs/2406.14994
The Black Hole Explorer (BHEX), an orbiting, multi-band, millimeter radio-telescope, in hybrid combination with millimeter terrestrial radio-telescopes, is designed to discover and measure the thin photon ring around the supermassive black holes M87*
Externí odkaz:
http://arxiv.org/abs/2406.11671
The gravitational perturbations of a rotating Kerr black hole are notoriously complicated, even at the linear level. In 1973, Teukolsky showed that their physical degrees of freedom are encoded in two gauge-invariant Weyl curvature scalars that obey
Externí odkaz:
http://arxiv.org/abs/2403.20311
The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax classical assumptions necessary for the estimation of
Externí odkaz:
http://arxiv.org/abs/2403.14385
Retinal fundus images play a crucial role in the early detection of eye diseases and, using deep learning approaches, recent studies have even demonstrated their potential for detecting cardiovascular risk factors and neurological disorders. However,
Externí odkaz:
http://arxiv.org/abs/2402.19186
Self-supervised learning methods based on data augmentations, such as SimCLR, BYOL, or DINO, allow obtaining semantically meaningful representations of image datasets and are widely used prior to supervised fine-tuning. A recent self-supervised learn
Externí odkaz:
http://arxiv.org/abs/2402.14566
Autor:
Beck, Jonas, Bosch, Nathanael, Deistler, Michael, Kadhim, Kyra L., Macke, Jakob H., Hennig, Philipp, Berens, Philipp
Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging. In particular, although ODEs are differentiable and would allow for gra
Externí odkaz:
http://arxiv.org/abs/2402.12231