Search Results for author:
Found 12 papers, 3 papers with code
Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases.
HACA3: A Unified Approach for Multi-site MR Image Harmonization
HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
Disentangling A Single MR Modality
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.
Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics
We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles.
Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning
First, a subset of AD patients and CN adults were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus AD).
Disentangling brain heterogeneity via semi-supervised deep-learning and MRI: dimensional representations of Alzheimer's Disease
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis.
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine.
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.
Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation
By indexing the dictionary, the whole brain probabilistic atlases adapt to each new subject quickly and can be used as spatial priors for visualization and processing.
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
Learning Implicit Brain MRI Manifolds with Deep Learning
Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5.
4D Multi-atlas Label Fusion using Longitudinal Images
(2) The proposed algorithm is a longitudinal generalization of a lead-ing joint label fusion method (JLF) that has proven adaptable to a wide variety of applications.
