Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Finn Behrendt"'
Autor:
Finn Behrendt, Marcel Bengs, Debayan Bhattacharya, Julia Krüger, Roland Opfer, Alexander Schlaefer
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Lung cancer is a serious disease responsible for millions of deaths every year. Early stages of lung cancer can be manifested in pulmonary lung nodules. To assist radiologists in reducing the number of overseen nodules and to increase the de
Externí odkaz:
https://doaj.org/article/df407fab13cb4fb1a6386b8457935d55
Autor:
Marcel Bengs, Finn Behrendt, Max-Heinrich Laves, Julia Krüger, Roland Opfer, Alexander Schlaefer
Publikováno v:
Medical Imaging 2022: Computer-Aided Diagnosis.
Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain M
Publikováno v:
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI).
The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UAD) methods have shown promising results for this task. These methods r
Autor:
Debayan Bhattacharya, Benjamin Tobias Becker, Finn Behrendt, Marcel Bengs, Dirk Beyersdorff, Dennis Eggert, Elina Petersen, Florian Jansen, Marvin Petersen, Bastian Cheng, Christian Betz, Alexander Schlaefer, Anna Sophie Hoffmann
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164361
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27b74af05442bdcd16f787115f1bd2f3
https://doi.org/10.1007/978-3-031-16437-8_41
https://doi.org/10.1007/978-3-031-16437-8_41
Autor:
Debayan Bhattacharya, Anna Sophie Hoffmann, Alexander Schlaefer, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Larissa Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz
Publikováno v:
Web of Science
Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05ae24b2dd3abd36afb11049ae4b74ba
Publikováno v:
International Journal of Computer Assisted Radiology and Surgery 16 (9): 1413-1423 (2021-09)
International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery
Purpose Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) have been proposed for the analysis of brain MRI. These methods rely on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c438b5ab6f65b4d4e7ea2d0720bf2b0d
https://hdl.handle.net/11420/10274
https://hdl.handle.net/11420/10274
Publikováno v:
Current Directions in Biomedical Engineering 1 (6): 20200024 (2020-05-01)
Current Directions in Biomedical Engineering, Vol 6, Iss 1, Pp 790-5 (2020)
Current Directions in Biomedical Engineering, Vol 6, Iss 1, Pp 790-5 (2020)
Robot-assisted minimally-invasive surgery is increasingly used in clinical practice. Force feedback offers potential to develop haptic feedback for surgery systems. Forces can be estimated in a vision-based way by capturing deformation observed in 2D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e42a88310c680c5c3974057d9b98b0c3
Publikováno v:
Current Directions in Biomedical Engineering 8 (1): 34-37 (2022-07)
Radiographs are a versatile diagnostic tool for the detection and assessment of pathologies, for treatment planning or for navigation and localization purposes in clinical interventions. However, their interpretation and assessment by radiologists ca