Model-Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning
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Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation.Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs.Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations.Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation techniqueComputer-aided diagnosis for diagnostically challenging breast lesions in DCE-MRI based on image registration and integration of morphologic and dynamic characteristics
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P2860
Model-Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning
description
article científic
@ca
article scientifique
@fr
articolo scientifico
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artigo científico
@pt
bilimsel makale
@tr
scientific article published on March 2008
@en
vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
@da
vědecký článek
@cs
name
Model-Free Visualization of Su ...... ised and Unsupervised Learning
@en
Model-Free Visualization of Su ...... sed and Unsupervised Learning.
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type
label
Model-Free Visualization of Su ...... ised and Unsupervised Learning
@en
Model-Free Visualization of Su ...... sed and Unsupervised Learning.
@nl
prefLabel
Model-Free Visualization of Su ...... ised and Unsupervised Learning
@en
Model-Free Visualization of Su ...... sed and Unsupervised Learning.
@nl
P2093
P2860
P1476
Model-Free Visualization of Su ...... ised and Unsupervised Learning
@en
P2093
Anke Meyer-Baese
Oliver Lange
Thorsten Twellmann
Tim W Nattkemper
P2860
P304
P356
10.1016/J.ENGAPPAI.2007.04.005
P577
2008-03-01T00:00:00Z