Precision diagnostics based on machine learning-derived imaging signatures
The complexity of modern multi-parametric MRI has increasingly challenged conventional
interpretations of such images. Machine learning has emerged as a powerful approach to …
interpretations of such images. Machine learning has emerged as a powerful approach to …
[HTML][HTML] Identification of tumor-specific MRI biomarkers using machine learning (ML)
The identification of reliable and non-invasive oncology biomarkers remains a main priority
in healthcare. There are only a few biomarkers that have been approved as diagnostic for …
in healthcare. There are only a few biomarkers that have been approved as diagnostic for …
Imaging biomarkers in oncology: basics and application to MRI
Cancer remains a global killer alongside cardiovascular disease. A better understanding of
cancer biology has transformed its management with an increasing emphasis on a …
cancer biology has transformed its management with an increasing emphasis on a …
Machine learning and imaging informatics in oncology
In the era of personalized and precision medicine, informatics technologies utilizing
machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in …
machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in …
Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis …
There is mounting evidence that assessment of multi-parametric magnetic resonance
imaging (mpMRI) profiles can noninvasively predict survival in many cancers, including …
imaging (mpMRI) profiles can noninvasively predict survival in many cancers, including …
Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection
Y Huang, PJG Lisboa, W El‐Deredy - Statistics in medicine, 2003 - Wiley Online Library
Magnetic resonance spectroscopy (MRS) provides a non‐invasive measurement of the
biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes …
biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes …
[HTML][HTML] Machine learning and glioma imaging biomarkers
AIM To review how machine learning (ML) is applied to imaging biomarkers in neuro-
oncology, in particular for diagnosis, prognosis, and treatment response monitoring …
oncology, in particular for diagnosis, prognosis, and treatment response monitoring …
[HTML][HTML] Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
Clinical trials increasingly employ medical imaging data in conjunction with supervised
classifiers, where the latter require large amounts of training data to accurately model the …
classifiers, where the latter require large amounts of training data to accurately model the …
Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
The growth of multiparametric imaging protocols has paved the way for quantitative imaging
phenotypes that predict treatment response and clinical outcome, reflect underlying cancer …
phenotypes that predict treatment response and clinical outcome, reflect underlying cancer …
Machine learning approaches in medical image analysis: From detection to diagnosis
M De Bruijne - Medical image analysis, 2016 - Elsevier
Abstract Machine learning approaches are increasingly successful in image-based
diagnosis, disease prognosis, and risk assessment. This paper highlights new research …
diagnosis, disease prognosis, and risk assessment. This paper highlights new research …