Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Axel Saalbach"'
Publikováno v:
Sensors, Vol 22, Iss 14, p 5195 (2022)
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for
Externí odkaz:
https://doaj.org/article/ea5d8e0281df47e0924d46a86656678b
Autor:
Jennifer Caffarel, Axel Saalbach, Alberto Bonomi, Joerg Habetha, Matthew Harris, Patrik Schauerte, Juan Perez Villacastin, Ramon Bover, Christian Zugck, Juergen Vogt, Arcadi García Alberola, John Cleland
Publikováno v:
International Journal of Integrated Care, Vol 12, Iss 4 (2012)
Externí odkaz:
https://doaj.org/article/3bd72d310c9b435daf231d59bba4865b
Autor:
Hrishikesh Deshpande, Axel Saalbach, Tim Harder, Edna Coetser, Shlomo Gotman, Thomas Buelow, Christian Wülker
Publikováno v:
Medical Imaging 2023: Image Processing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d2933e3fcf11ab7963d734558cca82b
https://doi.org/10.1117/12.2653986
https://doi.org/10.1117/12.2653986
Autor:
Harald Ittrich, Tobias Knopp, Ivo M. Baltruschat, Gerhard Adam, Michael Grass, Hannes Nickisch, Axel Saalbach, Leonhard Steinmeister
Publikováno v:
European Radiology
Objective The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c5f86c191a3f90d9ef2b854dd379e56
https://doi.org/10.1007/s00330-020-07480-7
https://doi.org/10.1007/s00330-020-07480-7
Autor:
Tom Brosch, Christopher S. Hall, Nathan M. Cross, Jalal B. Andre, Axel Saalbach, Karsten Sommer
Publikováno v:
AJNR Am J Neuroradiol
BACKGROUND AND PURPOSE: Motion artifacts are a frequent source of image degradation in the clinical application of MR imaging (MRI). Here we implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08c66e9d4e4402cbe7fe7252a6f4fe7e
https://doi.org/10.3174/ajnr.a6436
https://doi.org/10.3174/ajnr.a6436
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164392
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::139881092032338998193ae02ea6becd
https://doi.org/10.1007/978-3-031-16440-8_66
https://doi.org/10.1007/978-3-031-16440-8_66
Publikováno v:
International journal of computer assisted radiology and surgery. 17(6)
Chest X-ray is one of the most widespread examinations of the human body. In interventional radiology, its use is frequently associated with the need to visualize various tube-like objects, such as puncture needles, guiding sheaths, wires, and cathet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6d1ff35f1acf054e28c1518dbf8410b
https://pubmed.ncbi.nlm.nih.gov/35430716
https://pubmed.ncbi.nlm.nih.gov/35430716
Publikováno v:
ISBI
The placement of a central venous catheter (CVC) for venous access is a common clinical routine. Nonetheless, various clinical studies report that CVC insertions are unsuccessful in up to 20% of all cases. Among other, typical complications include t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9729b9ae26eb3f7481a44c41f6102936
https://doi.org/10.1109/isbi48211.2021.9434022
https://doi.org/10.1109/isbi48211.2021.9434022
Publikováno v:
Medical Imaging 2021: Image Processing.
In order to alleviate the risk of radiation induced cataract in patients undergoing head CT examinations, the guidelines published by the American Association of Physicists in Medicine (AAPM) link the optimal scan angle to particular anatomic landmar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f1fb46b838298be6d73ce11f3865ed5c
https://doi.org/10.1117/12.2581810
https://doi.org/10.1117/12.2581810
Publikováno v:
ISBI
In this paper, we propose a deep learning approach for the segmentation of body parts in computer tomography (CT) localizer images. Such images pose difficulties in the automatic image analysis on account of variable field-of-view, diverse patient po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73358827d4815ebfec2556737bc193b5
https://doi.org/10.1109/isbi45749.2020.9098429
https://doi.org/10.1109/isbi45749.2020.9098429