Adolescent Brain Cognitive Development Neurocognitive Prediction
First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings
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Kilian M. Pohl
Computer Science
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Chapter and Conference Paper
Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by MRI synthesis, for which Gener...
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Chapter and Conference Paper
Imputing Brain Measurements Across Data Sets via Graph Neural Networks
Publicly available data sets of structural MRIs might not contain specific measurements of brain Regions of Interests (ROIs) that are important for training machine learning models. For example, the curvature ...
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Chapter and Conference Paper
LSOR: Longitudinally-Consistent Self-Organized Representation Learning
Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces generated by deep learning via ...
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Chapter and Conference Paper
An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment
One of the hallmark symptoms of Parkinson’s Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain functio...
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Chapter and Conference Paper
One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation
One-shot federated learning (FL) has emerged as a promising solution in scenarios where multiple communication rounds are not practical. Notably, as feature distributions in medical data are less discriminativ...
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Chapter and Conference Paper
Bridging the Gap Between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing
A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an ou...
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Chapter and Conference Paper
Multiple Instance Neuroimage Transformer
For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classification of T1-weighted (T1w) ...
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Chapter and Conference Paper
Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome
The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscien...
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Chapter and Conference Paper
GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation
Parkinson’s disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture. PD is typically diagnosed by...
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Chapter and Conference Paper
A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models
Translating machine learning algorithms into clinical applications requires addressing challenges related to interpretability, such as accounting for the effect of confounding variables (or metadata). Confound...
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Chapter and Conference Paper
Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI
Subject motion during an MRI sequence can cause ghosting effects or diffuse image noise in the phase-encoding direction and hence is likely to bias findings in neuroimaging studies. Detecting motion artifacts ...
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Chapter and Conference Paper
Self-supervised Longitudinal Neighbourhood Embedding
Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyzing this data via machine learning generally requires a lar...
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Chapter and Conference Paper
Representation Disentanglement for Multi-modal Brain MRI Analysis
Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested that multi-modal deep learning a...
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Chapter and Conference Paper
Longitudinal Correlation Analysis for Decoding Multi-modal Brain Development
Starting from childhood, the human brain restructures and rewires throughout life. Characterizing such complex brain development requires effective analysis of longitudinal and multi-modal neuroimaging data. H...
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Chapter and Conference Paper
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier...
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Chapter and Conference Paper
Inpainting Cropped Diffusion MRI Using Deep Generative Models
Minor artifacts introduced during image acquisition are often negligible to the human eye, such as a confined field of view resulting in MRI missing the top of the head. This crop** artifact, however, can ca...
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Chapter and Conference Paper
Deep Parametric Mixtures for Modeling the Functional Connectome
Functional connectivity between brain regions is often estimated by correlating brain activity measured by resting-state fMRI in those regions. The impact of factors (e.g., disorder or substance use) are then ...
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Chapter and Conference Paper
Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis
The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs...
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Chapter and Conference Paper
Vision-Based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson’s Disease Motor Severity
Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD imp...
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Book and Conference Proceedings
