Search Results for author: Christian Wachinger

Found 39 papers, 16 papers with code

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

1 code implementation CVPR 2022 Fabian Bongratz, Anne-Marie Rickmann, Sebastian Pölsterl, Christian Wachinger

The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology.

Alzheimer's Disease Diagnosis via Deep Factorization Machine Models

no code implementations12 Aug 2021 Raphael Ronge, Kwangsik Nho, Christian Wachinger, Sebastian Pölsterl

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 knowledge about the interactions of biomarkers.

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

1 code implementation13 Jul 2021 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 image information for classifying patients.

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

1 code implementation13 Jul 2021 Sebastian Pölsterl, Christina Aigner, Christian Wachinger

We propose Shapley Value Explanation of Heterogeneous Neural Networks (SVEHNN) for explaining the Alzheimer's diagnosis made by a DNN from the 3D point cloud of the neuroanatomy and tabular biomarkers.

Decision Making

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

no code implementations23 Apr 2021 Fabian Gröger, Anne-Marie Rickmann, Christian Wachinger

We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty.

Unsupervised Domain Adaptation

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

no code implementations20 Apr 2021 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.


Recalibration of Neural Networks for Point Cloud Analysis

no code implementations25 Nov 2020 Ignacio Sarasua, Sebastian Poelsterl, Christian Wachinger

First, we demonstrate the benefit and versatility of our proposed modules by incorporating them into three state-of-the-art networks for 3D point cloud analysis: PointNet++, DGCNN, and RSCNN.

Semi-Structured Deep Piecewise Exponential Models

no code implementations11 Nov 2020 Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer

We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning.

Survival Analysis

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

no code implementations2 Oct 2020 Benjamin Gutierrez Becker, Ignacio Sarasua, Christian Wachinger

The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimers disease, to the point that they can be used to train a discriminative model for disease classification.

Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum

1 code implementation23 Jun 2020 Sebastian Pölsterl, Christian Wachinger

We derive a causal graph from the current clinical knowledge on cause and effect in the Alzheimer's disease continuum, and show that identifiability of the causal effect requires all confounders to be known and measured.

Causal Inference

Importance Driven Continual Learning for Segmentation Across Domains

2 code implementations30 Apr 2020 Sinan Özgür Özgün, Anne-Marie Rickmann, Abhijit Guha Roy, Christian Wachinger

The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications.

Brain Segmentation Continual Learning +1

Recalibrating 3D ConvNets with Project & Excite

1 code implementation25 Feb 2020 Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Christian Wachinger

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.

Brain Segmentation Computer Vision

Detect and Correct Bias in Multi-Site Neuroimaging Datasets

1 code implementation12 Feb 2020 Christian Wachinger, Anna Rieckmann, Sebastian Pölsterl

Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies.

Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference

no code implementations9 Jul 2019 Christian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann, Sebastian Pölsterl

In this work, we combine 12, 207 MRI scans from 15 studies and show that simple pooling is often ill-advised due to introducing various types of biases in the training data.

Causal Inference

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

2 code implementations11 Jun 2019 Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Nassir Navab, Christian Wachinger

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.

Brain Segmentation Semantic Segmentation

Adversarial Learned Molecular Graph Inference and Generation

1 code implementation24 May 2019 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.

Drug Discovery

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

no code implementations16 May 2019 Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, Christian Wachinger

A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients.

Brain Segmentation Federated Learning

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

no code implementations14 Jan 2019 Magdalini Paschali, Walter Simson, Abhijit Guha Roy, Muhammad Ferjad Naeem, Rüdiger Göbl, Christian Wachinger, Nassir Navab

Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.

Data Augmentation General Classification +2

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

2 code implementations24 Nov 2018 Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger

Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.

Brain Segmentation

InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation

no code implementations11 Oct 2018 Shubham Kumar, Sailesh Conjeti, Abhijit Guha Roy, Christian Wachinger, Nassir Navab

We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI images at iso-intense stage, which can be easily extended for other segmentation tasks involving multi-modalities.

Infant Brain Mri Segmentation MRI segmentation +1

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

4 code implementations23 Aug 2018 Abhijit Guha Roy, Nassir Navab, Christian Wachinger

Towards this end, we introduce three variants of SE modules for segmentation, (i) squeezing spatially and exciting channel-wise, (ii) squeezing channel-wise and exciting spatially and (iii) joint spatial and channel 'squeeze & excitation'.

Image Classification Semantic Segmentation

Keypoint Transfer for Fast Whole-Body Segmentation

no code implementations22 Jun 2018 Christian Wachinger, Matthew Toews, Georg Langs, William Wells, Polina Golland

We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images.

Semantic Segmentation

Deep Multi-Structural Shape Analysis: Application to Neuroanatomy

no code implementations4 Jun 2018 Benjamin Gutierrez-Becker, Christian Wachinger

We propose a deep neural network for supervised learning on neuroanatomical shapes.

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

no code implementations28 Apr 2018 Christian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann

Next, we introduce metrics to quantify the compatibility across datasets and to create embeddings of neuroimaging sites.

Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling

no code implementations19 Apr 2018 Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger

We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty.

Brain Segmentation Translation

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

no code implementations4 Apr 2018 Benjamin Gutierrez Becker, Tassilo Klein, Christian Wachinger

Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.

Age Estimation

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

10 code implementations7 Mar 2018 Abhijit Guha Roy, Nassir Navab, Christian Wachinger

Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.

Brain Segmentation Image Classification +1

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

6 code implementations12 Jan 2018 Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger

We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds.

Brain Segmentation Decision Making +1

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

no code implementations23 May 2017 Benjamín Gutiérrez, Loïc Peter, Tassilo Klein, Christian Wachinger

With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets.

DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy

no code implementations27 Feb 2017 Christian Wachinger, Martin Reuter, Tassilo Klein

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images.

Brain Segmentation Multi-class Classification +1

Sparse Projections of Medical Images onto Manifolds

no code implementations22 Mar 2013 George H. Chen, Christian Wachinger, Polina Golland

To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold.

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