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pro vyhledávání: '"Wismüller, A."'
In the present research, the effectiveness of large-scale Augmented Granger Causality (lsAGC) as a tool for gauging brain network connectivity was examined to differentiate between marijuana users and typical controls by utilizing resting-state funct
Externí odkaz:
http://arxiv.org/abs/2410.18506
Deep learning models can be applied successfully in real-work problems; however, training most of these models requires massive data. Recent methods use language and vision, but unfortunately, they rely on datasets that are not usually publicly avail
Externí odkaz:
http://arxiv.org/abs/2301.10951
It is a challenging research endeavor to infer causal relationships in multivariate observational time-series. Such data may be represented by graphs, where nodes represent time-series, and edges directed causal influence scores between them. If the
Externí odkaz:
http://arxiv.org/abs/2205.03029
There is an urgent need for streamlining radiology Quality Assurance (QA) programs to make them better and faster. Here, we present a novel approach, Artificial Intelligence (AI)-Based QUality Assurance by Restricted Investigation of Unequal Scores (
Externí odkaz:
http://arxiv.org/abs/2205.00629
Publikováno v:
In Computer Communications 1 August 2024 224:169-191
Autor:
Wismüller, Axel, Vosoughi, M. Ali
The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method uti
Externí odkaz:
http://arxiv.org/abs/2101.10471
Autor:
Wismüller, Axel, Stockmaster, Larry
Objective: To introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) in radiology. By tracking both large-scale utilization and AI results data, the tru-AI approach is designed to calculate surrogates for measurin
Externí odkaz:
http://arxiv.org/abs/2010.07437
To gain insight into complex systems it is a key challenge to infer nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only sh
Externí odkaz:
http://arxiv.org/abs/2009.04681
Publikováno v:
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 109503F, (2019)
Chest x-rays are the most common radiology studies for diagnosing lung and heart disease. Hence, a system for automated pre-reporting of pathologic findings on chest x-rays would greatly enhance radiologists' productivity. To this end, we investigate
Externí odkaz:
http://arxiv.org/abs/2006.06805
The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant t
Externí odkaz:
http://arxiv.org/abs/2004.01185