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Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning.
This article was published in the following journal.
Name: Journal of the American Medical Informatics Association : JAMIA
Trees in 3D images obtained from lidar were automatically extracted in the presence of other objects that were not trees. We proposed a method combining 3D image processing and machine learning techni...
Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteo...
Machine learning, particularly deep learning has boosted medical image analysis over the past years. Training a good model based on deep learning requires large amount of labelled data. However, it is...
Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether mac...
Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ruptured from unruptured aneurysms and identify features associa...
The investigators' goal is to develop a non-selective and non-invasive procedure to identify aggressive tumors and simultaneously identify their exact location in Prostate cancer patients ...
The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascu...
The Hamlet.rt study is a prospective data collection and patient questionnaire study for patients undergoing image-guided radiotherapy with curative intent. The aim of the study is to use...
Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review b...
Diffusion-weighted Whole Body Magnetic Resonance Imaging (WB-MRI) is a new technique that builds on existing Magnetic Resonance Imaging (MRI) technology. It uses the movement of water mole...
Composition of images of EARTH or other planets from data collected during SPACE FLIGHT by remote sensing instruments onboard SPACECRAFT. The satellite sensor systems measure and record absorbed, emitted, or reflected energy across the spectra, as well as global position and time.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.