
STRUDEL: SelfTraining with Uncertainty Dependent Label Refinement across Domains
We propose an unsupervised domain adaptation (UDA) approach for white ma...
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Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer's Disease
Geometric deep learning can find representations that are optimal for a ...
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Recalibration of Neural Networks for Point Cloud Analysis
Spatial and channel recalibration have become powerful concepts in comp...
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SemiStructured Deep Piecewise Exponential Models
We propose a versatile framework for survival analysis that combines adv...
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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 ...
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Controlling for Unknown Confounders in Neuroimaging
The aim of many studies in biomedicine is to infer causeeffect relation...
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Importance Driven Continual Learning for Segmentation Across Domains
The ability of neural networks to continuously learn and adapt to new ta...
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Recalibrating 3D ConvNets with Project Excite
Fully Convolutional Neural Networks (FCNNs) achieve stateoftheart pe...
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Detect and Correct Bias in MultiSite Neuroimaging Datasets
The desire to train complex machine learning algorithms and to increase ...
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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...
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Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
Neuroimaging datasets keep growing in size to address increasingly compl...
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`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Fully Convolutional Neural Networks (FCNNs) achieve stateoftheart pe...
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LikelihoodFree Inference and Generation of Molecular Graphs
Recent methods for generating novel molecules use graph representations ...
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BrainTorrent: A PeertoPeer Environment for Decentralized Federated Learning
Access to sufficient annotated data is a common challenge in training de...
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'Squeeze & Excite' Guided FewShot Segmentation of Volumetric Images
Deep neural networks enable highly accurate image segmentation, but requ...
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Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
In this paper we propose a novel augmentation technique that improves no...
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Bayesian QuickNAT: Model Uncertainty in Deep WholeBrain Segmentation for Structurewise Quality Control
We introduce Bayesian QuickNAT for the automated quality control of whol...
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InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
We present a novel, parameterefficient and practical fully convolutiona...
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Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks
In a wide range of semantic segmentation tasks, fully convolutional neur...
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Deep Shape Analysis on Abdominal Organs for Diabetes Prediction
Morphological analysis of organs based on images is a key task in medica...
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Keypoint Transfer for Fast WholeBody Segmentation
We introduce an approach for image segmentation based on sparse correspo...
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Deep MultiStructural Shape Analysis: Application to Neuroanatomy
We propose a deep neural network for supervised learning on neuroanatomi...
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Detect, Quantify, and Incorporate Dataset Bias: A Neuroimaging Analysis on 12,207 Individuals
Neuroimaging datasets keep growing in size to address increasingly compl...
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Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling
We introduce inherent measures for effective quality control of brain se...
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Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality
Multivariate regression models for age estimation are a powerful tool fo...
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Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
Fully convolutional neural networks (FCNNs) have set the stateofthea...
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QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds
Whole brain segmentation from structural magnetic resonance imaging is a...
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A MultiArmed Bandit to Smartly Select a Training Set from Big Medical Data
With the availability of big medical image data, the selection of an ade...
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Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Training deep fully convolutional neural networks (FCNNs) for semantic ...
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ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Optical coherence tomography (OCT) is used for noninvasive diagnosis of...
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DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
We introduce DeepNAT, a 3D Deep convolutional neural network for the aut...
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Diverse Landmark Sampling from Determinantal Point Processes for Scalable Manifold Learning
High computational costs of manifold learning prohibit its application f...
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Sparse Projections of Medical Images onto Manifolds
Manifold learning has been successfully applied to a variety of medical ...
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Christian Wachinger
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