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06/27/2023
Meshes Meet Voxels: Abdominal Organ Segmentation via Diffeomorphic Deformations
Abdominal multi-organ segmentation from CT and MRI is an essential prere...
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03/14/2023
HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
The wide range of research in deep learning-based medical image segmenta...
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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...
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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...
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07/05/2022
CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis
Modeling temporal changes in subcortical structures is crucial for a bet...
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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...
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09/01/2021
TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes
The longitudinal modeling of neuroanatomical changes related to Alzheime...
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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...
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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...
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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...
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04/23/2021
STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains
We propose an unsupervised domain adaptation (UDA) approach for white ma...
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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 ...
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11/25/2020
Recalibration of Neural Networks for Point Cloud Analysis
Spatial and channel re-calibration have become powerful concepts in comp...
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11/11/2020
Semi-Structured Deep Piecewise Exponential Models
We propose a versatile framework for survival analysis that combines adv...
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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 ...
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06/23/2020
Controlling for Unknown Confounders in Neuroimaging
The aim of many studies in biomedicine is to infer cause-effect relation...
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04/30/2020
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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02/25/2020
Recalibrating 3D ConvNets with Project Excite
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art pe...
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02/12/2020
Detect and Correct Bias in Multi-Site Neuroimaging Datasets
The desire to train complex machine learning algorithms and to increase ...
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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...
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07/09/2019
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
Neuroimaging datasets keep growing in size to address increasingly compl...
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06/11/2019
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art pe...
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05/24/2019
Likelihood-Free Inference and Generation of Molecular Graphs
Recent methods for generating novel molecules use graph representations ...
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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...
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02/04/2019
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
Deep neural networks enable highly accurate image segmentation, but requ...
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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...
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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...
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10/11/2018
InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
We present a novel, parameter-efficient and practical fully convolutiona...
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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...
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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...
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06/22/2018
Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse correspo...
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06/04/2018
Deep Multi-Structural Shape Analysis: Application to Neuroanatomy
We propose a deep neural network for supervised learning on neuroanatomi...
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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...
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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...
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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...
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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...
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01/12/2018
QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds
Whole brain segmentation from structural magnetic resonance imaging is a...
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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...
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05/02/2017
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Training deep fully convolutional neural networks (F-CNNs) for semantic ...
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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...
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02/27/2017
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
We introduce DeepNAT, a 3D Deep convolutional neural network for the aut...
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03/11/2015
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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03/22/2013
