Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.
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Imaging Techniques for Clinical Burn Assessment with a Focus on Multispectral ImagingMultispectral and Photoplethysmography Optical Imaging Techniques Identify Important Tissue Characteristics in an Animal Model of Tangential Burn Excision.Machine learning and computer vision approaches for phenotypic profilingClassification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier.Burn-injured tissue detection for debridement surgery through the combination of non-invasive optical imaging techniques.
P2860
Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Outlier detection and removal ...... ctral burn diagnostic imaging.
@en
type
label
Outlier detection and removal ...... ctral burn diagnostic imaging.
@en
prefLabel
Outlier detection and removal ...... ctral burn diagnostic imaging.
@en
P2093
P1476
Outlier detection and removal ...... ctral burn diagnostic imaging.
@en
P2093
Eric W Sellke
J Michael DiMaio
Jeffrey E Thatcher
John J Squiers
Weirong Mo
Wensheng Fan
P304
P356
10.1117/1.JBO.20.12.121305
P577
2015-12-01T00:00:00Z