Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Löhr, Timo"'
We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows uncertainty to be quantified at the individual class level, thereby improv
Externí odkaz:
http://arxiv.org/abs/2406.02354
Autor:
Prabhakar, Chinmay, Li, Hongwei Bran, Paetzold, Johannes C., Loehr, Timo, Niu, Chen, Mühlau, Mark, Rueckert, Daniel, Wiestler, Benedikt, Menze, Bjoern
Multiple Sclerosis (MS) is a severe neurological disease characterized by inflammatory lesions in the central nervous system. Hence, predicting inflammatory disease activity is crucial for disease assessment and treatment. However, MS lesions can occ
Externí odkaz:
http://arxiv.org/abs/2308.16863
Autor:
Kofler, Florian, Shit, Suprosanna, Ezhov, Ivan, Fidon, Lucas, Horvath, Izabela, Al-Maskari, Rami, Li, Hongwei, Bhatia, Harsharan, Loehr, Timo, Piraud, Marie, Erturk, Ali, Kirschke, Jan, Peeken, Jan C., Vercauteren, Tom, Zimmer, Claus, Wiestler, Benedikt, Menze, Bjoern
Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can t
Externí odkaz:
http://arxiv.org/abs/2205.08209
Autor:
Mächler, Leon, Ezhov, Ivan, Kofler, Florian, Shit, Suprosanna, Paetzold, Johannes C., Loehr, Timo, Wiestler, Benedikt, Menze, Bjoern
We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community. Our method addresses the pro
Externí odkaz:
http://arxiv.org/abs/2111.08649
Autor:
Li, Hongwei, Loehr, Timo, Sekuboyina, Anjany, Zhang, Jianguo, Wiestler, Benedikt, Menze, Bjoern
In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying models in an unseen data domain, e.g. a new centreor a new scanner. I
Externí odkaz:
http://arxiv.org/abs/2001.09313
Autor:
Timmins, Kimberley M., van der Schaaf, Irene C., Bennink, Edwin, Ruigrok, Ynte M., An, Xingle, Baumgartner, Michael, Bourdon, Pascal, De Feo, Riccardo, Noto, Tommaso Di, Dubost, Florian, Fava-Sanches, Augusto, Feng, Xue, Giroud, Corentin, Group, Inteneural, Hu, Minghui, Jaeger, Paul F., Kaiponen, Juhana, Klimont, Michał, Li, Yuexiang, Li, Hongwei, Lin, Yi, Loehr, Timo, Ma, Jun, Maier-Hein, Klaus H., Marie, Guillaume, Menze, Bjoern, Richiardi, Jonas, Rjiba, Saifeddine, Shah, Dhaval, Shit, Suprosanna, Tohka, Jussi, Urruty, Thierry, Walińska, Urszula, Yang, Xiaoping, Yang, Yunqiao, Yin, Yin, Velthuis, Birgitta K., Kuijf, Hugo J.
Publikováno v:
In NeuroImage September 2021 238
