189 citations · 645 across the 47 of their papers we have counts for
12 papers · 1 filter
Body Fat Estimation from Surface Meshes using Graph Neural Networks
Tamara T. Mueller, Siyu Zhou, Sophie Starck +7
Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Fre…
Interpretable 2D Vision Models for 3D Medical Images
Alexander Ziller, Ayhan Can Erdur, Marwa Trigui +9
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly highe…
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl +6
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are requ…
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann +3
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural netw…
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising…
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Felix Meissen, Johannes Paetzold, Georgios Kaissis +1
Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the ano…