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Found 10 papers, 2 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.
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
(2) Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains.
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields.
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.
DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI
We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia.
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention.
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images.
Ranked #19 on
3D Instance Segmentation
on ScanNet(v2)
(mAP @ 50 metric)
