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Computed tomography lung parenchymal descriptions in routine radiological reporting have diagnostic and prognostic utility in patients with idiopathic pulmonary arterial hypertension and pulmonary hypertension associated with lung disease
Background: Patients with pulmonary hypertension (PH) and lung disease may pose a diagnostic dilemma between idiopathic pulmonary …
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Mixed-order spectral clustering for complex networks
Spectral clustering (SC) is a popular approach for gaining insights from complex networks. Conventional SC focuses on second-order …
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Probabilistic rank-one tensor analysis with concurrent regularizations
Subspace learning for tensors attracts increasing interest in recent years, leading to the development of multilinear extensions of …
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Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge
Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the …
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Pulmonary hypertension in association with lung disease: quantitative CT and artificial intelligence to the rescue? state-of-the-art review
Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, …
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A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis
Aims: Pulmonary arterial hypertension (PAH) is a progressive condition with high mortality. Quantitative cardiovascular magnetic …
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Joint interaction with context operation for collaborative filtering
In recommender systems, the classical matrix factorization model for collaborative filtering only considers joint interactions between …
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Feature extraction for incomplete data via low-rank tensor decomposition with feature regularization
Multidimensional data (i.e., tensors) with missing entries are common in practice. Extracting features from incomplete tensors is an …
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Rank-one matrix completion with automatic rank estimation via L1-norm regularization
Completing a matrix from a small subset of its entries, i.e., matrix completion is a challenging problem arising from many real-world …
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Motor imagery classification based on bilinear sub-manifold learning of symmetric positive-definite matrices
In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram …
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