207 citations · 480 across the 15 of their papers we have counts for
16 papers
Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease
Tom Nuno Wolf, Sebastian Pölsterl, Christian Wachinger
Alzheimer's disease (AD) has a complex and multifactorial etiology, which requires integrating information about neuroanatomy, genetics, and cerebrospinal fluid biomarkers for accu…
Joint Reconstruction and Parcellation of Cortical Surfaces
Anne-Marie Rickmann, Fabian Bongratz, Sebastian Pölsterl +2
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative…
CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis
Ignacio Sarasua, Sebastian Pölsterl, Christian Wachinger
Modeling temporal changes in subcortical structures is crucial for a better understanding of the progression of Alzheimer's disease (AD). Given their flexibility to adapt to hetero…
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…
Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform
Sebastian Pölsterl, Tom Nuno Wolf, Christian Wachinger
Prior work on diagnosing Alzheimer's disease from magnetic resonance images of the brain established that convolutional neural networks (CNNs) can leverage the high-dimensional ima…
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…
Recalibration of Neural Networks for Point Cloud Analysis
Ignacio Sarasua, Sebastian Poelsterl, Christian Wachinger
Spatial and channel re-calibration have become powerful concepts in computer vision. Their ability to capture long-range dependencies is especially useful for those networks that e…
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…
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum
Sebastian Pölsterl, Christian Wachinger
Studying the relationship between neuroanatomy and cognitive decline due to Alzheimer's has been a major research focus in the last decade. However, to infer cause-effect relations…
Detect and Correct Bias in Multi-Site Neuroimaging Datasets
Christian Wachinger, Anna Rieckmann, Sebastian Pölsterl
The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The m…
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…
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl +2
Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult fo…
Adversarial Learned Molecular Graph Inference and Generation
Sebastian Pölsterl, Christian Wachinger
Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference. However, trainin…
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl +2
Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this…
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Sebastian Pölsterl, Nassir Navab, Amin Katouzian
Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large a…