Support vector machines for diagnosis of breast tumors on US images.
about
Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformaticsAn Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor ImagesNeuroimaging-based biomarkers in psychiatry: clinical opportunities of a paradigm shift.Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectivenessClassification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines.Pre-operative prediction of advanced prostatic cancer using clinical decision support systems: accuracy comparison between support vector machine and artificial neural network.Quantification of heterogeneity as a biomarker in tumor imaging: a systematic reviewA new endoscopic ultrasonography image processing method to evaluate the prognosis for pancreatic cancer treated with interstitial brachytherapy.Comparative analysis of logistic regression, support vector machine and artificial neural network for the differential diagnosis of benign and malignant solid breast tumors by the use of three-dimensional power Doppler imaging.A clinical decision support system for femoral peripheral arterial disease treatment.Ultrasound image segmentation and tissue characterization.Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features.Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer.Development of a Nomogram to Predict N2 or N3 Stage in T1-2 Invasive Breast Cancer Patients with No Palpable Lymphadenopathy.Explanation-aware computing of the prognosis for breast cancer supported by IK-DCBRC: Technical innovation.Individualized positioning for maximum heart protection during breast irradiation.Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT.Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.Classification of benign and malignant breast masses based on shape and texture features in sonography images.Automatic breast parenchymal density classification integrated into a CADe system.Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D EchocardiographyDiagnostic Accuracy of Different Machine Learning Algorithms for Breast Cancer Risk Calculation: a Meta-Analysis
P2860
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P2860
Support vector machines for diagnosis of breast tumors on US images.
description
2003 nî lūn-bûn
@nan
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
2003年论文
@zh
2003年论文
@zh-cn
name
Support vector machines for diagnosis of breast tumors on US images.
@en
type
label
Support vector machines for diagnosis of breast tumors on US images.
@en
prefLabel
Support vector machines for diagnosis of breast tumors on US images.
@en
P2093
P1433
P1476
Support vector machines for diagnosis of breast tumors on US images.
@en
P2093
Dar-Ren Chen
Ruey-Feng Chang
Wen-Jie Wu
Woo Kyung Moon
Yi-Hong Chou
P304
P356
10.1016/S1076-6332(03)80044-2
P577
2003-02-01T00:00:00Z