no code implementations • 16 Nov 2023 • Raphael Schäfer, Till Nicke, Henning Höfener, Annkristin Lange, Dorit Merhof, Friedrich Feuerhake, Volkmar Schulz, Johannes Lotz, Fabian Kiessling
The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models.
no code implementations • 3 Feb 2023 • Stephan Naunheim, Yannick Kuhl, David Schug, Volkmar Schulz, Florian Mueller
Our work addresses this challenge by combining traditional calibration with AI and residual physics, presenting a highly promising approach.
1 code implementation • 22 Nov 2021 • Tianyu Han, Jakob Nikolas Kather, Federico Pedersoli, Markus Zimmermann, Sebastian Keil, Maximilian Schulze-Hagen, Marc Terwoelbeck, Peter Isfort, Christoph Haarburger, Fabian Kiessling, Volkmar Schulz, Christiane Kuhl, Sven Nebelung, Daniel Truhn
We present a generic solution for this problem by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation optimization approach.
no code implementations • MICCAI Workshop COMPAY 2021 • Narmin Ghaffari Laleh, Amelie Echle, Hannah Sophie Muti, Katherine Jane Hewitt, Volkmar Schulz, Jakob Nikolas Kather
Digitized histopathology slides contain a wealth of information, only a fraction of which is being used in clinical routine.
1 code implementation • 25 Nov 2020 • Tianyu Han, Sven Nebelung, Federico Pedersoli, Markus Zimmermann, Maximilian Schulze-Hagen, Michael Ho, Christoph Haarburger, Fabian Kiessling, Christiane Kuhl, Volkmar Schulz, Daniel Truhn
Contrary to previous research on adversarially trained models, we found that the accuracy of such models was equal to standard models when sufficiently large datasets and dual batch norm training were used.