Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

scientific article published on 10 January 2017

Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture is …
instance of (P31):
scholarly articleQ13442814

External links are
P356DOI10.3897/RIO.3.E11731

P2093author name stringRodrigo Riveros
Rodrigo Salas
Steren Chabert
Alejandro Veloz
Maximiliano Godoy
Pablo Cox
Tomás Mardones
P2860cites workNon-parametric geodesic active regions: method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA.Q51025315
Nonlinear anisotropic stress analysis of anatomically realistic cerebral aneurysms.Q51083481
Morphological characterization of intracranial aneurysms using 3-D moment invariants.Q51905204
Physical factors effecting cerebral aneurysm pathophysiologyQ36920195
Artificial neural networks and prostate cancer--tools for diagnosis and management.Q38080685
@neurIST complex information processing toolchain for the integrated management of cerebral aneurysms.Q39607475
Machine Learning in Medical ImagingQ42056164
Quantified aneurysm shape and rupture riskQ48987117
P275copyright licenseCreative Commons Attribution 4.0 InternationalQ20007257
P6216copyright statuscopyrightedQ50423863
P921main subjectmachine learningQ2539
feature extractionQ1026626
P304page(s)e11731
P577publication date2017-01-10
P1433published inResearch Ideas and OutcomesQ20895800
P1476titleApplying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture
P478volume3

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Q91632785Predictive analytics and machine learning in stroke and neurovascular medicinecites workP2860

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