367 citations · 696 across the 18 of their papers we have counts for
9 papers · 1 filter
Alzheimer's Disease Diagnosis via Deep Factorization Machine Models
Raphael Ronge, Kwangsik Nho, Christian Wachinger +1
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting kn…
Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data
Sebastian Pölsterl, Christina Aigner, Christian Wachinger
Deep Neural Networks (DNNs) have an enormous potential to learn from complex biomedical data. In particular, DNNs have been used to seamlessly fuse heterogeneous information from n…
Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer's Disease
Ignacio Sarasua, Jonwong Lee, Christian Wachinger
Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations. While current work has ma…
Semi-Structured Deep Piecewise Exponential Models
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger +3
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential…
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data
Sebastian Pölsterl, Ignacio Sarasua, Benjamín Gutiérrez-Becker +1
We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and…
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
Christian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann +1
Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex mach…