Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Sebastian Gündel"'
Autor:
Kai Packhäuser, Sebastian Gündel, Nicolas Münster, Christopher Syben, Vincent Christlein, Andreas Maier
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data
Externí odkaz:
https://doaj.org/article/0958d8a3fbd7422bb6f24acdac00b7af
Publikováno v:
Bildverarbeitung für die Medizin 2021 ISBN: 9783658331979
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ef3086253c53b584bd6cb2e69355a02
https://doi.org/10.1007/978-3-658-33198-6_68
https://doi.org/10.1007/978-3-658-33198-6_68
Autor:
Kai Packhäuser, Sebastian Gündel, Nicolas Münster, Christopher Syben, Vincent Christlein, Andreas Maier
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ed09e4b68d362b7cea3e307d14d46a8
Autor:
Sebastian Gündel, Andreas Maier
Publikováno v:
Informatik aktuell ISBN: 9783658292669
Bildverarbeitung für die Medizin
Bildverarbeitung für die Medizin
The current accessibility to large medical datasets for training convolutional neural networks is tremendously high. The associated dataset labels are always considered to be the real “ground truth”. However, the labeling procedures often seem to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fde690da6a67ef98da0fcf9aafd042ca
https://doi.org/10.1007/978-3-658-29267-6_64
https://doi.org/10.1007/978-3-658-29267-6_64
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030598600
Bildverarbeitung für die Medizin
Bildverarbeitung für die Medizin
Chest X-ray (CXR) is the most common examination for fast detection of pulmonary abnormalities. Recently, automated algorithms have been developed to classify multiple diseases and abnormalities in CXR scans. However, because of the limited availabil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c161aaae69d4e8c3c1e246449f43d073
https://doi.org/10.1007/978-3-030-59861-7_51
https://doi.org/10.1007/978-3-030-59861-7_51
Autor:
Dorin Comaniciu, Arnaud Arindra Adiyoso Setio, Andreas Maier, Sasa Grbic, Florin C. Ghesu, Bogdan Georgescu, Sebastian Gündel
Publikováno v:
Medical Image Analysis. 72:102087
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::feddd4225f4152ce9d9f50822364fef8
https://doi.org/10.1016/j.media.2021.102087
https://doi.org/10.1016/j.media.2021.102087
Publikováno v:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783030134686
CIARP
CIARP
Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74e5ec26bd7ecc3e96c48a1a39a16892
https://doi.org/10.1007/978-3-030-13469-3_88
https://doi.org/10.1007/978-3-030-13469-3_88