AIMC Topic: Machine Learning

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Micro-morphological feature visualization, auto-classification, and evolution quantitative analysis of tumors by using SR-PCT.

Cancer medicine
Tissue micro-morphological abnormalities and interrelated quantitative data can provide immediate evidences for tumorigenesis and metastasis in microenvironment. However, the multiscale three-dimensional nondestructive pathological visualization, mea...

Cardiac Operative Risk in Latin America: A Comparison of Machine Learning Models vs EuroSCORE-II.

The Annals of thoracic surgery
BACKGROUND: Machine learning is a useful tool for predicting medical outcomes. This study aimed to develop a machine learning-based preoperative score to predict cardiac surgical operative mortality.

Dual self-paced multi-view clustering.

Neural networks : the official journal of the International Neural Network Society
By utilizing the complementary information from multiple views, multi-view clustering (MVC) algorithms typically achieve much better clustering performance than conventional single-view methods. Although in this field, great progresses have been made...

Classification of red blood cell aggregation using empirical wavelet transform analysis of ultrasonic radiofrequency echo signals.

Ultrasonics
Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machin...

TractLearn: A geodesic learning framework for quantitative analysis of brain bundles.

NeuroImage
Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies ...

MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes.

Journal of computational chemistry
Machine Learning-assisted Lipid Phase Analysis (MLLPA) is a new Python 3 module developed to analyze phase domains in a lipid membrane based on lipid molecular states. Reading standard simulation coordinate and trajectory files, the software first an...

R.ROSETTA: an interpretable machine learning framework.

BMC bioinformatics
BACKGROUND: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a pr...

Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids.

Computational intelligence and neuroscience
Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual ins...

Machine Learning Compared With Conventional Statistical Models for Predicting Myocardial Infarction Readmission and Mortality: A Systematic Review.

The Canadian journal of cardiology
BACKGROUND: Machine learning (ML) methods are increasingly used in addition to conventional statistical modelling (CSM) for predicting readmission and mortality in patients with myocardial infarction (MI). However, the two approaches have not been sy...

Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach.

Archives of toxicology
Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., DILI signatures. In contrast, others are manifested as broader types of liver injury. The variety of ...