Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Häntze, Hartmut"'
Autor:
Dorfner, Felix J., Vahldiek, Janis L., Donle, Leonhard, Zhukov, Andrei, Xu, Lina, Häntze, Hartmut, Makowski, Marcus R., Aerts, Hugo J. W. L., Proft, Fabian, Rodriguez, Valeria Rios, Rademacher, Judith, Protopopov, Mikhail, Haibel, Hildrun, Diekhoff, Torsten, Torgutalp, Murat, Adams, Lisa C., Poddubnyy, Denis, Bressem, Keno K.
Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. Methods: This retrospective multicenter study included conventional pelvic radiograph
Externí odkaz:
http://arxiv.org/abs/2405.07369
Autor:
Häntze, Hartmut, Xu, Lina, Dorfner, Felix J., Donle, Leonhard, Truhn, Daniel, Aerts, Hugo, Prokop, Mathias, van Ginneken, Bram, Hering, Alessa, Adams, Lisa C., Bressem, Keno K.
Purpose: To introduce a deep learning model capable of multi-organ segmentation in MRI scans, offering a solution to the current limitations in MRI analysis due to challenges in resolution, standardized intensity values, and variability in sequences.
Externí odkaz:
http://arxiv.org/abs/2405.06463
Autor:
Häntze, Hartmut, Xu, Lina, Rattunde, Maximilian, Donle, Leonhard, Dorfner, Felix J., Hering, Alessa, Adams, Lisa C., Bressem, Keno K.
Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show that a simple image inversion technique can significantly improve the
Externí odkaz:
http://arxiv.org/abs/2405.03713
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