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This article was published in the following journal.
Name: Magnetic resonance in medicine
Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here ...
Machine learning algorithms are used successfully in this paper to forecast reliably upcoming short-term high concentration episodes, or peaks (
Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (au...
To evaluate the repeatability of the new spectral domain optical coherence tomography (HOCT-1F), and also to evaluate the agreement between vertical and horizontal scan protocols. In addition, we also...
The aim of this study was to determine the accuracy and repeatability of the shoulder abduction test and to assess the effect of transection of the medial shoulder support structures in canine cada...
Longitudinal, observational study to assess the short-term (test re-test) and mid-term (within the span of 28 days) repeatability of active anterior rhinomanometry (AAR) measures on 4 para...
Evaluate the repeatability and reproducibility of the RTVue-XR for measuring the total corneal thickness (pachymetry), the epithelial thickness, and the stromal thickness mapping in normal...
"Within-day and Between-day Repeatability of the Breath Pattern in Healthy Children and in Children With Moderate or Severe Asthma" is an observational prospective study in outpatient clin...
The purpose of this preliminary substudy to the parent study "Aerobic Exercise Intervention for Knee Osteoarthritis" is to determine the repeatability and reproducibility of various measur...
This study aims to prospectively assess the repeatability and reproducibility of iron-corrected T1 (cT1), T2*, and hepatic proton density fat fraction (PDFF) quantification with multiparam...
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.
Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)
Bladder Cancer Brain Cancer Breast Cancer Cancer Cervical Cancer Colorectal Head & Neck Cancers Hodgkin Lymphoma Leukemia Lung Cancer Melanoma Myeloma Ovarian Cancer Pancreatic Cancer ...
Prostate cancer (cancer de prostata) Prostate cancer (cancer de prostata) is a form of cancer that develops in the prostate, a gland in the male reproductive system. Most prostate cancers are slow growing; however, there are cases of aggressive prostat...