Sebastian Pölsterl | DeepAI

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∙ 03/13/2023

Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease

Alzheimer's disease (AD) has a complex and multifactorial etiology, whic...

13 Tom Nuno Wolf, et al. ∙

∙ 07/05/2022

Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs

Alzheimer's Disease (AD) is the most common form of dementia and often d...

27 Marla Narazani, et al. ∙

∙ 07/05/2022

CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis

Modeling temporal changes in subcortical structures is crucial for a bet...

10 Ignacio Sarasua, et al. ∙

∙ 03/17/2022

Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks

The reconstruction of cortical surfaces from brain magnetic resonance im...

16 Fabian Bongratz, et al. ∙

∙ 09/01/2021

TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes

The longitudinal modeling of neuroanatomical changes related to Alzheime...

7 Ignacio Sarasua, et al. ∙

∙ 08/12/2021

Alzheimer's Disease Diagnosis via Deep Factorization Machine Models

The current state-of-the-art deep neural networks (DNNs) for Alzheimer's...

3 Raphael Ronge, et al. ∙

∙ 07/13/2021

Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data

Deep Neural Networks (DNNs) have an enormous potential to learn from com...

20 Sebastian Pölsterl, et al. ∙

∙ 07/13/2021

Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform

Prior work on diagnosing Alzheimer's disease from magnetic resonance ima...

15 Sebastian Pölsterl, et al. ∙

∙ 11/11/2020

Semi-Structured Deep Piecewise Exponential Models

We propose a versatile framework for survival analysis that combines adv...

12 Philipp Kopper, et al. ∙

∙ 06/23/2020

Controlling for Unknown Confounders in Neuroimaging

The aim of many studies in biomedicine is to infer cause-effect relation...

0 Sebastian Pölsterl, et al. ∙

∙ 02/12/2020

Detect and Correct Bias in Multi-Site Neuroimaging Datasets

The desire to train complex machine learning algorithms and to increase ...

0 Christian Wachinger, et al. ∙

∙ 09/09/2019

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progressio...

0 Sebastian Pölsterl, et al. ∙

∙ 07/09/2019

Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference

Neuroimaging datasets keep growing in size to address increasingly compl...

0 Christian Wachinger, et al. ∙

∙ 05/24/2019

Likelihood-Free Inference and Generation of Molecular Graphs

Recent methods for generating novel molecules use graph representations ...

0 Sebastian Pölsterl, et al. ∙

∙ 05/16/2019

BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

Access to sufficient annotated data is a common challenge in training de...

0 Abhijit Guha Roy, et al. ∙

∙ 02/04/2019

'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images

Deep neural networks enable highly accurate image segmentation, but requ...

10 Abhijit Guha Roy, et al. ∙

∙ 11/21/2016

An Efficient Training Algorithm for Kernel Survival Support Vector Machines

Survival analysis is a fundamental tool in medical research to identify ...

0 Sebastian Pölsterl, et al. ∙

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