1 citations · 1 across the 1 of their papers we have counts for
6 papers
Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks
Dmitrii Seletkov, Paul Hager, Georgios Kaissis +3
Survival analysis is crucial for many medical applications, but remains challenging for modern machine learning due to limited data, censoring, and the heterogeneity of tabular cov…
Are foundation models useful feature extractors for electroencephalography analysis?
Ãzgün Turgut, Felix S. Bott, Markus Ploner +1
The success of foundation models in natural language processing and computer vision has motivated similar approaches in time series analysis. While foundational time series models…
A Master Class on Reproducibility: A Student Hackathon on Advanced MRI Reconstruction Methods
Lina Felsner, Sevgi G. Kafali, Hannah Eichhorn +9
We report the design, protocol, and outcomes of a student reproducibility hackathon focused on replicating the results of three influential MRI reconstruction papers: (a) MoDL, an…
Gradient-Weight Alignment as a Train-Time Proxy for Generalization in Classification Tasks
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Robust validation metrics remain essential in contemporary deep learning, not only to detect overfitting and poor generalization, but also to monitor training dynamics. In the supe…
GReAT: leveraging geometric artery data to improve wall shear stress assessment
Julian Suk, Jolanda J. Wentzel, Patryk Rygiel +3
Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed…
SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Experiments
Simon Dahan, Gabriel Bénédict, Logan Z. J. Williams +4
Current AI frameworks for brain decoding and encoding, typically train and test models within the same datasets. This limits their utility for brain computer interfaces (BCI) or ne…