Predicting pediatric optic pathway glioma progression using advanced magnetic resonance image analysis and machine learning

Comparison of Machine Learning Techniques for Magnetic Resonance Image Analysis

Magnetic resonance imaging (MRI) is a powerful non-invasive medical imaging technique that encodes the mechanical, physiological and chemical structure of soft tissues. However, manual segmentation of tissue regions of interest (ROIs) can be a laborious process prone to operator error. In this project, we compared algorithms from 3 classes of supervised machine learning (ML) techniques for MRI ...

Predicting Phospholipidosis Using Machine Learning

Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the impor...

synthesis of amido alkylnaphthols using nano-magnetic particles and surfactants

we used dbsa and nano-magnetic for the synthesis of amido alkylnaphtols.

Advanced Motion Correction and Image Reconstruction for Cardiac Magnetic Resonance

Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis

A Machine Learning (ML) System known as ROAMS (Ranker for Open-Auto Maintenance Scheduling) was developed to create failure-susceptibility rankings for almost one thousand 13.8kV-27kV energy distribution feeder cables that supply electricity to the boroughs of New York City. In Manhattan, rankings are updated every 20 minutes and displayed on distribution system operators’ screens. Additionally...