3 citations · 3 across the 4 of their papers we have counts for
5 papers
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
Simon Süwer, Julian Klemm, Elisa Acitelli +39
Federated learning enables collaborative training without sharing patient-level data, but most studies remain simulations. Based on five requirements derived from the literature, w…
A Privacy-Preserving Ecosystem for Developing Machine Learning Algorithms Using Patient Data: Insights from the TUM.ai Makeathon
Simon Süwer, Mai Khanh Mai, Christoph Klein +4
The integration of clinical data offers significant potential for the development of personalized medicine. However, its use is severely restricted by the General Data Protection R…
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image Classification
Keyvan RahimiZadeh, Ahmad Taheri, Jan Baumbach +5
Lithology discrimination is a crucial activity in characterizing oil reservoirs, and processing lithology microscopic images is an essential technique for investigating fossils and…
Batch effects can impair federated learning in multi-center omics studies
Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann +5
Federated learning (FL) enables collaborative analysis of biomedical data without exchanging sensitive patient-level information, but its performance in multi-center studies may be…
UnPaSt: unsupervised patient stratification by biclustering of omics data
Michael Hartung, Andreas Maier, Yuliya Burankova +25
Unsupervised patient stratification is essential for disease subtype discovery, yet, despite growing evidence of molecular heterogeneity of non-oncological diseases, popular method…