Zobrazeno 1 - 10
of 1 573
pro vyhledávání: '"Denzinger A"'
Autor:
Denzinger, Felix, Wels, Michael, Taubmann, Oliver, Kordon, Florian, Wagner, Fabian, Mehltretter, Stephanie, Gülsün, Mehmet A., Schöbinger, Max, André, Florian, Buss, Sebastian, Görich, Johannes, Sühling, Michael, Maier, Andreas
Coronary artery disease (CAD) is often treated minimally invasively with a catheter being inserted into the diseased coronary vessel. If a patient exhibits a Shepherd's Crook (SC) Right Coronary Artery (RCA) - an anatomical norm variant of the corona
Externí odkaz:
http://arxiv.org/abs/2306.01752
Autor:
Marcinkevičs, Ričards, Wolfertstetter, Patricia Reis, Klimiene, Ugne, Chin-Cheong, Kieran, Paschke, Alyssia, Zerres, Julia, Denzinger, Markus, Niederberger, David, Wellmann, Sven, Ozkan, Ece, Knorr, Christian, Vogt, Julia E.
Publikováno v:
Medical Image Analysis, 91, 103042 (2024)
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, d
Externí odkaz:
http://arxiv.org/abs/2302.14460
Autor:
Thies, Mareike, Wagner, Fabian, Maul, Noah, Folle, Lukas, Meier, Manuela, Rohleder, Maximilian, Schneider, Linda-Sophie, Pfaff, Laura, Gu, Mingxuan, Utz, Jonas, Denzinger, Felix, Manhart, Michael, Maier, Andreas
Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise k
Externí odkaz:
http://arxiv.org/abs/2212.02177
Image annotation is one essential prior step to enable data-driven algorithms. In medical imaging, having large and reliably annotated data sets is crucial to recognize various diseases robustly. However, annotator performance varies immensely, thus
Externí odkaz:
http://arxiv.org/abs/2211.06146
Autor:
Wagner, Fabian, Thies, Mareike, Pfaff, Laura, Aust, Oliver, Pechmann, Sabrina, Weidner, Daniela, Maul, Noah, Rohleder, Maximilian, Gu, Mingxuan, Utz, Jonas, Denzinger, Felix, Maier, Andreas
Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low-dose acquisitions. However, considerably less research investigates m
Externí odkaz:
http://arxiv.org/abs/2211.01111
Autor:
Mühlberg, Alexander, Ritter, Paul, Langer, Simon, Goossens, Chloë, Nübler, Stefanie, Schneidereit, Dominik, Taubmann, Oliver, Denzinger, Felix, Nörenberg, Dominik, Haug, Michael, Goldmann, Wolfgang H., Maier, Andreas K., Friedrich, Oliver, Kreiss, Lucas
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that
Externí odkaz:
http://arxiv.org/abs/2210.16273
Autor:
Dr. Wiebke Eisler, Prof. Dr. Manuel Held, Prof. Dr. Afshin Rahmanian-Schwarz, Dr. Jan-Ole Baur, Prof. Dr. Adrien Daigeler, Dr. Markus Denzinger
Publikováno v:
JPRAS Open, Vol 40, Iss , Pp 336-345 (2024)
Background: Deep dermal wounds in extensive burns and chronic wound-healing disorders represent a significant medical problem and require a high level of therapy to reduce the risk of infection and other long-term consequences, such as amputation. A
Externí odkaz:
https://doaj.org/article/d9a7482061ae42c6b29f8e2f879f4040
Robust and reliable anonymization of chest radiographs constitutes an essential step before publishing large datasets of such for research purposes. The conventional anonymization process is carried out by obscuring personal information in the images
Externí odkaz:
http://arxiv.org/abs/2209.11531
Autor:
Wagner, Fabian, Thies, Mareike, Denzinger, Felix, Gu, Mingxuan, Patwari, Mayank, Ploner, Stefan, Maul, Noah, Pfaff, Laura, Huang, Yixing, Maier, Andreas
Publikováno v:
Sci.Rep. 12 (2022) 17540
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning~(DL)-based methods were introduced, outperforming conventional denoisin
Externí odkaz:
http://arxiv.org/abs/2207.07368
Reliably detecting diseases using relevant biological information is crucial for real-world applicability of deep learning techniques in medical imaging. We debias deep learning models during training against unknown bias - without preprocessing/filt
Externí odkaz:
http://arxiv.org/abs/2205.13297