Zobrazeno 1 - 10
of 406
pro vyhledávání: '"A. Perkonigg"'
Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition settings. When training a deep learning model on an initial static training
Externí odkaz:
http://arxiv.org/abs/2111.13069
Autor:
Perkonigg, Matthias, Mesenbrink, Peter, Goehler, Alexander, Martic, Miljen, Ba-Ssalamah, Ahmed, Langs, Georg
In multi-center randomized clinical trials imaging data can be diverse due to acquisition technology or scanning protocols. Models predicting future outcome of patients are impaired by this data heterogeneity. Here, we propose a prediction method tha
Externí odkaz:
http://arxiv.org/abs/2111.07634
Autor:
Sobotka, Daniel, Herold, Alexander, Perkonigg, Matthias, Beer, Lucian, Bastati, Nina, Sablatnig, Alina, Ba-Ssalamah, Ahmed, Langs, Georg
Publikováno v:
In Computerized Medical Imaging and Graphics June 2024 114
Imaging in clinical routine is subject to changing scanner protocols, hardware, or policies in a typically heterogeneous set of acquisition hardware. Accuracy and reliability of deep learning models suffer from those changes as data and targets becom
Externí odkaz:
http://arxiv.org/abs/2106.03351
Autor:
Hofmanninger, Johannes, Perkonigg, Matthias, Brink, James A., Pianykh, Oleg, Herold, Christian, Langs, Georg
In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance. Scanner manufacturer, reconstruction kernel, dose, other protocol specific settings or administering of contrast agents are ex
Externí odkaz:
http://arxiv.org/abs/2007.02639
Predictive marker patterns in imaging data are a means to quantify disease and progression, but their identification is challenging, if the underlying biology is poorly understood. Here, we present a method to identify predictive texture patterns in
Externí odkaz:
http://arxiv.org/abs/2002.03721
Autor:
Kavur, A. Emre, Gezer, N. Sinem, Barış, Mustafa, Aslan, Sinem, Conze, Pierre-Henri, Groza, Vladimir, Pham, Duc Duy, Chatterjee, Soumick, Ernst, Philipp, Özkan, Savaş, Baydar, Bora, Lachinov, Dmitry, Han, Shuo, Pauli, Josef, Isensee, Fabian, Perkonigg, Matthias, Sathish, Rachana, Rajan, Ronnie, Sheet, Debdoot, Dovletov, Gurbandurdy, Speck, Oliver, Nürnberger, Andreas, Maier-Hein, Klaus H., Akar, Gözde Bozdağı, Ünal, Gözde, Dicle, Oğuz, Selver, M. Alper
Publikováno v:
Med. Image Anal. 69 (2021) 101950
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. In order to expand t
Externí odkaz:
http://arxiv.org/abs/2001.06535
Autor:
Licandro, Roxane, Hofmanninger, Johannes, Perkonigg, Matthias, Röhrich, Sebastian, Weber, Marc-André, Wennmann, Markus, Kintzele, Laurent, Piraud, Marie, Menze, Bjoern, Langs, Georg
The reliable and timely stratification of bone lesion evolution risk in smoldering Multiple Myeloma plays an important role in identifying prime markers of the disease's advance and in improving the patients' outcome. In this work we provide an asymm
Externí odkaz:
http://arxiv.org/abs/1907.13539
Autor:
Matthias Perkonigg, Johannes Hofmanninger, Christian J. Herold, James A. Brink, Oleg Pianykh, Helmut Prosch, Georg Langs
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a
Externí odkaz:
https://doaj.org/article/f9fc7f5d825149758843a206d878d863
Publikováno v:
Advanced Manufacturing: Polymer & Composites Science, Vol 6, Iss 1, Pp 29-37 (2020)
This work describes a model-based methodology to improve the bonding quality between the metal and composite constituents of one-shot-hybrid resin transfer moulding (OSH-RTM) parts. In order to reduce void induced defects in the interface an ideal fl
Externí odkaz:
https://doaj.org/article/76a2e54a201c4a5cba39d68a11b89bd8