Christian Wachinger

research

∙ 06/27/2023

Meshes Meet Voxels: Abdominal Organ Segmentation via Diffeomorphic Deformations

Abdominal multi-organ segmentation from CT and MRI is an essential prere...

0 Fabian Bongratz, et al. ∙

research

∙ 03/14/2023

HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery

The wide range of research in deep learning-based medical image segmenta...

15 Anne-Marie Rickmann, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 04/23/2021

STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains

We propose an unsupervised domain adaptation (UDA) approach for white ma...

14 Fabian Gröger, et al. ∙

research

∙ 04/20/2021

Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer's Disease

Geometric deep learning can find representations that are optimal for a ...

20 Ignacio Sarasua, et al. ∙

research

∙ 11/25/2020

Recalibration of Neural Networks for Point Cloud Analysis

Spatial and channel re-calibration have become powerful concepts in comp...

7 Ignacio Sarasua, et al. ∙

research

∙ 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. ∙

research

∙ 10/02/2020

Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks

We introduce deep neural networks for the analysis of anatomical shapes ...

0 Benjamín Gutiérrez-Becker, et al. ∙

research

∙ 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. ∙

research

∙ 04/30/2020

Importance Driven Continual Learning for Segmentation Across Domains

The ability of neural networks to continuously learn and adapt to new ta...

0 Sinan Özgür Özgün, et al. ∙

research

∙ 02/25/2020

Recalibrating 3D ConvNets with Project Excite

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art pe...

0 Anne-Marie Rickmann, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 06/11/2019

`Project & Excite' Modules for Segmentation of Volumetric Medical Scans

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art pe...

0 Anne-Marie Rickmann, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 01/14/2019

Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness

In this paper we propose a novel augmentation technique that improves no...

0 Magdalini Paschali, et al. ∙

research

∙ 11/24/2018

Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control

We introduce Bayesian QuickNAT for the automated quality control of whol...

0 Abhijit Guha Roy, et al. ∙

research

∙ 10/11/2018

InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation

We present a novel, parameter-efficient and practical fully convolutiona...

0 Shubham Kumar, et al. ∙

research

∙ 08/23/2018

Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks

In a wide range of semantic segmentation tasks, fully convolutional neur...

4 Abhijit Guha Roy, et al. ∙

research

∙ 08/06/2018

Deep Shape Analysis on Abdominal Organs for Diabetes Prediction

Morphological analysis of organs based on images is a key task in medica...

0 Benjamín Gutiérrez-Becker, et al. ∙

research

∙ 06/22/2018

Keypoint Transfer for Fast Whole-Body Segmentation

We introduce an approach for image segmentation based on sparse correspo...

4 Christian Wachinger, et al. ∙

research

∙ 06/04/2018

Deep Multi-Structural Shape Analysis: Application to Neuroanatomy

We propose a deep neural network for supervised learning on neuroanatomi...

0 Benjamín Gutiérrez-Becker, et al. ∙

research

∙ 04/28/2018

Detect, Quantify, and Incorporate Dataset Bias: A Neuroimaging Analysis on 12,207 Individuals

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

0 Christian Wachinger, et al. ∙

research

∙ 04/19/2018

Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling

We introduce inherent measures for effective quality control of brain se...

0 Abhijit Guha Roy, et al. ∙

research

∙ 04/04/2018

Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality

Multivariate regression models for age estimation are a powerful tool fo...

0 Benjamín Gutiérrez-Becker, et al. ∙

research

∙ 03/07/2018

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

Fully convolutional neural networks (F-CNNs) have set the state-of-the-a...

0 Abhijit Guha Roy, et al. ∙

research

∙ 01/12/2018

QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds

Whole brain segmentation from structural magnetic resonance imaging is a...

0 Abhijit Guha Roy, et al. ∙

research

∙ 05/23/2017

A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data

With the availability of big medical image data, the selection of an ade...

0 Benjamín Gutiérrez, et al. ∙

research

∙ 05/02/2017

Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data

Training deep fully convolutional neural networks (F-CNNs) for semantic ...

0 Abhijit Guha Roy, et al. ∙

research

∙ 04/07/2017

ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network

Optical coherence tomography (OCT) is used for non-invasive diagnosis of...

0 Abhijit Guha Roy, et al. ∙

research

∙ 02/27/2017

DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy

We introduce DeepNAT, a 3D Deep convolutional neural network for the aut...

1 Christian Wachinger, et al. ∙

research

∙ 03/11/2015

Diverse Landmark Sampling from Determinantal Point Processes for Scalable Manifold Learning

High computational costs of manifold learning prohibit its application f...

0 Christian Wachinger, et al. ∙

research

∙ 03/22/2013

Sparse Projections of Medical Images onto Manifolds

Manifold learning has been successfully applied to a variety of medical ...

0 George H. Chen, et al. ∙

Christian Wachinger

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