AIMC Topic: Machine Learning

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A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis.

Journal of medical systems
This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of ...

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

Journal of medical systems
Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the...

Machine Learning for Precision Psychiatry: Opportunities and Challenges.

Biological psychiatry. Cognitive neuroscience and neuroimaging
The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit...

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.

Medical image analysis
During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improve...

In silico toxicity profiling of natural product compound libraries from African flora with anti-malarial and anti-HIV properties.

Computational biology and chemistry
This paper describes an analysis of the diversity and chemical toxicity assessment of three chemical libraries of compounds from African flora (the p-ANAPL, AfroMalariaDb, and Afro-HIV), respectively containing compounds exhibiting activities against...

Manifold optimization-based analysis dictionary learning with an ℓ-norm regularizer.

Neural networks : the official journal of the International Neural Network Society
Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solution...

A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV)....

A deep learning method for lincRNA detection using auto-encoder algorithm.

BMC bioinformatics
BACKGROUND: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep ...

Identifying genotype-phenotype relationships in biomedical text.

Journal of biomedical semantics
BACKGROUND: One important type of information contained in biomedical research literature is the newly discovered relationships between phenotypes and genotypes. Because of the large quantity of literature, a reliable automatic system to identify thi...