no code implementations • 12 Mar 2024 • Michael Götz, Christian Weber, Franciszek Binczyk, Joanna Polanska, Rafal Tarnawski, Barbara Bobek-Billewicz, Ullrich Köthe, Jens Kleesiek, Bram Stieltjes, Klaus H. Maier-Hein
We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation.
no code implementations • 12 Mar 2024 • Michael Götz, Christian Weber, Christoph Kolb, Klaus Maier-Hein
In machine learning larger databases are usually associated with higher classification accuracy due to better generalization.
no code implementations • 5 Mar 2024 • Hasan Abu-Rasheed, Christian Weber, Madjid Fathi
In the era of personalized education, the provision of comprehensible explanations for learning recommendations is of a great value to enhance the learner's understanding and engagement with the recommended learning content.
no code implementations • 24 Jan 2024 • Hasan Abu-Rasheed, Mareike Dornhöfer, Christian Weber, Gábor Kismihók, Ulrike Buchmann, Madjid Fathi
While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms.
no code implementations • 16 Jan 2024 • Hasan Abu-Rasheed, Mohamad Hussam Abdulsalam, Christian Weber, Madjid Fathi
Student commitment towards a learning recommendation is not separable from their understanding of the reasons it was recommended to them; and their ability to modify it based on that understanding.
no code implementations • 18 May 2021 • Hasan Abu-Rasheed, Christian Weber, Johannes Zenkert, Roland Krumm, Madjid Fathi
A key challenge of this sustainability is the intelligent management of the generated data, as well as the knowledge extracted from it, in order to utilize this knowledge for improving future procedures.