2 citations · 3 across the 5 of their papers we have counts for
6 papers
Federated unsupervised random forest for privacy-preserving patient stratification
Bastian Pfeifer, Christel Sirocchi, Marcus D. Bloice +2
In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques appl…
Parea: multi-view ensemble clustering for cancer subtype discovery
Bastian Pfeifer, Marcus D. Bloice, Michael G. Schimek
Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods has be…
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Julian Matschinske, Julian Späth, Reza Nasirigerdeh +29
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is o…
Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs
Marcus D. Bloice, Peter M. Roth, Andreas Holzinger
In this paper a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images…
Patch augmentation: Towards efficient decision boundaries for neural networks
Marcus D. Bloice, Peter M. Roth, Andreas Holzinger
In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial at…
Augmentor: An Image Augmentation Library for Machine Learning
Marcus D. Bloice, Christof Stocker, Andreas Holzinger
The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and…