31 Result(s)
within
Computer Science
Image Processing and Computer Vision
Kilian M. Pohl
Chapter
Conference Paper
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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
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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Chapter and Conference Paper
Confounder-Aware Visualization of ConvNets
With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better understanding of how a disease impac...
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Chapter and Conference Paper
Variational AutoEncoder for Regression: Application to Brain Aging Analysis
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified...
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Chapter and Conference Paper
Data Augmentation Based on Substituting Regional MRIs Volume Scores
Due to difficulties in collecting sufficient training data, recent advances in neural-network-based methods have not been fully explored in the analysis of brain Magnetic Resonance Imaging (MRI). A possible so...
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Chapter and Conference Paper
Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis
Resting-state functional connectivity states are often identified as clusters of dynamic connectivity patterns. However, existing clustering approaches do not distinguish major states from rarely occurring min...
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Chapter and Conference Paper
Multi-label Transduction for Identifying Disease Comorbidity Patterns
Study of the untoward effects associated with the comorbidity of multiple diseases on brain morphology requires identifying differences across multiple diagnostic grou**s. To identify such effects and differ...
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Chapter and Conference Paper
End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification
As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, d...
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Chapter and Conference Paper
A Riemannian Framework for Longitudinal Analysis of Resting-State Functional Connectivity
Even though the number of longitudinal resting-state-fMRI studies is increasing, accurately characterizing the changes in functional connectivity across visits is a largely unexplored topic. To improve charact...
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Chapter and Conference Paper
3D Motion Modeling and Reconstruction of Left Ventricle Wall in Cardiac MRI
The analysis of left ventricle (LV) wall motion is a critical step for understanding cardiac functioning mechanisms and clinical diagnosis of ventricular diseases. We present a novel approach for 3D motion mod...
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Chapter and Conference Paper
Joint Data Harmonization and Group Cardinality Constrained Classification
To boost the power of classifiers, studies often increase the size of existing samples through the addition of independently collected data sets. Doing so requires harmonizing the data for demographic and acqu...
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Chapter and Conference Paper
Solving Logistic Regression with Group Cardinality Constraints for Time Series Analysis
We propose an algorithm to distinguish 3D+t images of healthy from diseased subjects by solving logistic regression based on cardinality constrained, group sparsity. This method reduces the risk of overfitting...
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Chapter and Conference Paper
FLOOR: Fusing Locally Optimal Registrations
Most registration algorithms, such as Demons [1], align two scans by iteratively finding the deformation minimizing the image dissimilarity at each location and smoothing this minimum across the image domain. ...
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Chapter and Conference Paper
Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models
Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation m...
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Chapter and Conference Paper
Extracting Evolving Pathologies via Spectral Clustering
A bottleneck in the analysis of longitudinal MR scans with white matter brain lesions is the temporally consistent segmentation of the pathology. We identify pathologies in 3D+t(ime) within a spectral graph cl...
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Chapter and Conference Paper
Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering
The clustering of fibers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and th...
