35,142 research outputs found
Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis
Multi-template based brain morphometric pattern analysis using magnetic resonance imaging (MRI) has been recently proposed for automatic diagnosis of Alzheimer’s disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multi-view morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multi-template based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality the underlying data distribution is actually not pre-known. In this paper, we propose an inherent structure based multi-view leaning (ISML) method using multiple templates for AD/MCI classification. Specifically, we first extract multi-view feature representations for subjects using multiple selected templates, and then cluster subjects within a specific class into several sub-classes (i.e., clusters) in each view space. Then, we encode those sub-classes with unique codes by considering both their original class information and their own distribution information, followed by a multi-task feature selection model. Finally, we learn an ensemble of view-specific support vector machine (SVM) classifiers based on their respectively selected features in each view, and fuse their results to draw the final decision. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multi-template based methods
Supervised machine learning based multi-task artificial intelligence classification of retinopathies
Artificial intelligence (AI) classification holds promise as a novel and
affordable screening tool for clinical management of ocular diseases. Rural and
underserved areas, which suffer from lack of access to experienced
ophthalmologists may particularly benefit from this technology. Quantitative
optical coherence tomography angiography (OCTA) imaging provides excellent
capability to identify subtle vascular distortions, which are useful for
classifying retinovascular diseases. However, application of AI for
differentiation and classification of multiple eye diseases is not yet
established. In this study, we demonstrate supervised machine learning based
multi-task OCTA classification. We sought 1) to differentiate normal from
diseased ocular conditions, 2) to differentiate different ocular disease
conditions from each other, and 3) to stage the severity of each ocular
condition. Quantitative OCTA features, including blood vessel tortuosity (BVT),
blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel
density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour
irregularity (FAZ-CI) were fully automatically extracted from the OCTA images.
A stepwise backward elimination approach was employed to identify sensitive
OCTA features and optimal-feature-combinations for the multi-task
classification. For proof-of-concept demonstration, diabetic retinopathy (DR)
and sickle cell retinopathy (SCR) were used to validate the supervised machine
leaning classifier. The presented AI classification methodology is applicable
and can be readily extended to other ocular diseases, holding promise to enable
a mass-screening platform for clinical deployment and telemedicine.Comment: Supplemental material attached at the en
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
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