Sample-size determination methodologies for machine learning in medical imaging research: a systematic review
I Balki, A Amirabadi, J Levman… - Canadian …, 2019 - journals.sagepub.com
Purpose The required training sample size for a particular machine learning (ML) model
applied to medical imaging data is often unknown. The purpose of this study was to provide …
applied to medical imaging data is often unknown. The purpose of this study was to provide …
[HTML][HTML] Radiomics and deep learning for disease detection in musculoskeletal radiology: an overview of novel MRI-and CT-based approaches
Radiomics and machine learning–based methods offer exciting opportunities for improving
diagnostic performance and efficiency in musculoskeletal radiology for various tasks …
diagnostic performance and efficiency in musculoskeletal radiology for various tasks …
Natural and artificial intelligence in neurosurgery: a systematic review
JT Senders, O Arnaout, AV Karhade… - …, 2018 - journals.lww.com
BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows
computer algorithms to learn from experience without being explicitly programmed …
computer algorithms to learn from experience without being explicitly programmed …
Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images?
Purpose To determine the accuracy of texture analysis to differentiate fat-poor
angiomyolipoma (fp-AML) from renal cell carcinoma (RCC) on unenhanced computed …
angiomyolipoma (fp-AML) from renal cell carcinoma (RCC) on unenhanced computed …
Machine learning in breast MRI
Machine‐learning techniques have led to remarkable advances in data extraction and
analysis of medical imaging. Applications of machine learning to breast MRI continue to …
analysis of medical imaging. Applications of machine learning to breast MRI continue to …
[HTML][HTML] Neurosurgery and artificial intelligence
M Mofatteh - AIMS neuroscience, 2021 - ncbi.nlm.nih.gov
Neurosurgeons receive extensive and lengthy training to equip themselves with various
technical skills, and neurosurgery require a great deal of pre-, intra-and postoperative …
technical skills, and neurosurgery require a great deal of pre-, intra-and postoperative …
Texture analysis as a radiomic marker for differentiating renal tumors
Purpose To evaluate the utility of texture analysis for the differentiation of renal tumors,
including the various renal cell carcinoma subtypes and oncocytoma. Materials and …
including the various renal cell carcinoma subtypes and oncocytoma. Materials and …
Texture analysis and machine learning for detecting myocardial infarction in noncontrast low-dose computed tomography: unveiling the invisible
M Mannil, J von Spiczak, R Manka… - Investigative …, 2018 - journals.lww.com
Objectives The aim of this study was to test whether texture analysis and machine learning
enable the detection of myocardial infarction (MI) on non–contrast-enhanced low radiation …
enable the detection of myocardial infarction (MI) on non–contrast-enhanced low radiation …
Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review
Background Many techniques are proposed for the quantification of tumor heterogeneity as
an imaging biomarker for differentiation between tumor types, tumor grading, response …
an imaging biomarker for differentiation between tumor types, tumor grading, response …
Diagnostic accuracy of MRI texture analysis for grading gliomas
A Ditmer, B Zhang, T Shujaat, A Pavlina… - Journal of Neuro …, 2018 - Springer
Purpose Texture analysis (TA) can quantify variations in surface intensity or patterns,
including some that are imperceptible to the human visual system. The purpose of this study …
including some that are imperceptible to the human visual system. The purpose of this study …