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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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11/11/2020
Semi-Structured Deep Piecewise Exponential Models
We propose a versatile framework for survival analysis that combines adv...
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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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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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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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11/21/2016