Zobrazeno 1 - 10
of 3 977
pro vyhledávání: '"Bogner, P."'
Autor:
Dong, Siyuan, Cai, Zhuotong, Hangel, Gilbert, Bogner, Wolfgang, Widhalm, Georg, Huang, Yaqing, Liang, Qinghao, You, Chenyu, Kumaragamage, Chathura, Fulbright, Robert K., Mahajan, Amit, Karbasi, Amin, Onofrey, John A., de Graaf, Robin A., Duncan, James S.
Publikováno v:
Medical Image Analysis (2024): 103358
Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases, cancers and diabetes. High spatial resolution MRSI is needed to charact
Externí odkaz:
http://arxiv.org/abs/2410.19288
Autor:
Weiser, Paul, Langs, Georg, Motyka, Stanislav, Bogner, Wolfgang, Courvoisier, Sébastien, Hoffmann, Malte, Klauser, Antoine, Andronesi, Ovidiu C.
Purpose. Proton Magnetic Resonance Spectroscopic Imaging (1H-MRSI) provides non-invasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain 1H-MRSI are spectral overlap of metabolite peaks with large lipid signal f
Externí odkaz:
http://arxiv.org/abs/2410.00746
Autor:
Weiser, Paul, Langs, Georg, Bogner, Wolfgang, Motyka, Stanislav, Strasser, Bernhard, Golland, Polina, Singh, Nalini, Dietrich, Jorg, Uhlmann, Erik, Batchelor, Tracy, Cahill, Daniel, Hoffmann, Malte, Klauser, Antoine, Andronesi, Ovidiu C.
Introduction: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp
Externí odkaz:
http://arxiv.org/abs/2409.18303
Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new qual
Externí odkaz:
http://arxiv.org/abs/2409.07192
Autor:
Omar, Rafiullah, Bogner, Justus, Muccini, Henry, Lago, Patricia, Martínez-Fernández, Silverio, Franch, Xavier
Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ensemble learning, i.e., forwarding the same request to several models and
Externí odkaz:
http://arxiv.org/abs/2407.02914
The study of nuclei at finite temperature is of immense interest for many areas of nuclear astrophysics and nuclear-reaction science. A variety of ab initio methods are now available for computing the properties of nuclei from interactions rooted in
Externí odkaz:
http://arxiv.org/abs/2407.00576
Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects. While there is a large variety of available tools usable for MLOps, th
Externí odkaz:
http://arxiv.org/abs/2406.19847
Autor:
Omar, Rafiullah, Bogner, Justus, Leest, Joran, Stoico, Vincenzo, Lago, Patricia, Muccini, Henry
ML-enabled systems that are deployed in a production environment typically suffer from decaying model prediction quality through concept drift, i.e., a gradual change in the statistical characteristics of a certain real-world domain. To combat this,
Externí odkaz:
http://arxiv.org/abs/2404.19452
Recent advances in artificial intelligence (AI) capabilities have increased the eagerness of companies to integrate AI into software systems. While AI can be used to have a positive impact on several dimensions of sustainability, this is often oversh
Externí odkaz:
http://arxiv.org/abs/2404.03995
RESTful APIs based on HTTP are one of the most important ways to make data and functionality available to applications and software services. However, the quality of the API design strongly impacts API understandability and usability, and many rules
Externí odkaz:
http://arxiv.org/abs/2402.13710