Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Bengs, Marcel"'
Autor:
Sogancioglu, Ecem, van Ginneken, Bram, Behrendt, Finn, Bengs, Marcel, Schlaefer, Alexander, Radu, Miron, Xu, Di, Sheng, Ke, Scalzo, Fabien, Marcus, Eric, Papa, Samuele, Teuwen, Jonas, Scholten, Ernst Th., Schalekamp, Steven, Hendrix, Nils, Jacobs, Colin, Hendrix, Ward, Sánchez, Clara I, Murphy, Keelin
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lu
Externí odkaz:
http://arxiv.org/abs/2401.02192
Autor:
Bhattacharya, Debayan, Becker, Benjamin Tobias, Behrendt, Finn, Bengs, Marcel, Beyersdorff, Dirk, Eggert, Dennis, Petersen, Elina, Jansen, Florian, Petersen, Marvin, Cheng, Bastian, Betz, Christian, Schlaefer, Alexander, Hoffmann, Anna Sophie
Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume, shape and other parameters like local contrast. However due to limit
Externí odkaz:
http://arxiv.org/abs/2209.01937
Autor:
Behrendt, Finn, Bengs, Marcel, Rogge, Frederik, Krüger, Julia, Opfer, Roland, Schlaefer, Alexander
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
Externí odkaz:
http://arxiv.org/abs/2204.05778
Autor:
Neidhardt, Maximilian, Bengs, Marcel, Latus, Sarah, Gerlach, Stefan, Cyron, Christian J., Sprenger, Johanna, Schlaefer, Alexander
Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elasticity value, which neglects information such as the shape of the propaga
Externí odkaz:
http://arxiv.org/abs/2204.05745
Autor:
Bengs, Marcel, Behrendt, Finn, Laves, Max-Heinrich, Krüger, Julia, Opfer, Roland, Schlaefer, Alexander
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
Externí odkaz:
http://arxiv.org/abs/2201.13081
Medulloblastoma (MB) is a primary central nervous system tumor and the most common malignant brain cancer among children. Neuropathologists perform microscopic inspection of histopathological tissue slides under a microscope to assess the severity of
Externí odkaz:
http://arxiv.org/abs/2109.06547
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:
http://arxiv.org/abs/2109.06540
Medulloblastoma (MB) is the most common malignant brain tumor in childhood. The diagnosis is generally based on the microscopic evaluation of histopathological tissue slides. However, visual-only assessment of histopathological patterns is a tedious
Externí odkaz:
http://arxiv.org/abs/2109.05025
Autor:
Bengs, Marcel, Gessert, Nils, Laffers, Wiebke, Eggert, Dennis, Westermann, Stephan, Mueller, Nina A., Gerstner, Andreas O. H., Betz, Christian, Schlaefer, Alexander
Early detection of cancerous tissue is crucial for long-term patient survival. In the head and neck region, a typical diagnostic procedure is an endoscopic intervention where a medical expert manually assesses tissue using RGB camera images. While he
Externí odkaz:
http://arxiv.org/abs/2007.01042
Tracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high spatial and temporal resolution. Recently, 3D convolutional neural ne
Externí odkaz:
http://arxiv.org/abs/2007.01044