Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

about

Optimizing area under the ROC curve using semi-supervised learning Computer-aided detection of exophytic renal lesions on non-contrast CT images

P2860

Q34482907-3665BF99-D22B-4368-8FC4-079E3162D03C Q34603467-04F27980-8BE8-47BD-9848-6E649C48EA0A

P2860

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

Item

http://www.wikidata.org/entity/Q36345942

description

2012 nî lūn-bûn

@nan

2012年の論文

@ja

2012年学术文章

@wuu

2012年学术文章

@zh-cn

2012年学术文章

@zh-hans

2012年学术文章

@zh-my

2012年学术文章

@zh-sg

2012年學術文章

@yue

2012年學術文章

@zh

2012年學術文章

@zh-hant

name

Seeing is believing: video cla ...... ng multiple-instance learning.

@ast

Seeing is believing: video cla ...... ng multiple-instance learning.

@en

type

Item

label

Seeing is believing: video cla ...... ng multiple-instance learning.

@ast

Seeing is believing: video cla ...... ng multiple-instance learning.

@en

prefLabel

Seeing is believing: video cla ...... ng multiple-instance learning.

@ast

Seeing is believing: video cla ...... ng multiple-instance learning.

@en

P1476

Seeing is believing: video cla ...... ng multiple-instance learning.

@en

P2093

Joseph E Burns

http://www.w3.org/2001/XMLSchema#string

Matthew T McKenna

http://www.w3.org/2001/XMLSchema#string

Nicholas Petrick

http://www.w3.org/2001/XMLSchema#string

Ronald M Summers

http://www.w3.org/2001/XMLSchema#string

Shijun Wang

http://www.w3.org/2001/XMLSchema#string

Tan B Nguyen

http://www.w3.org/2001/XMLSchema#string

P2860

Cancer statistics, 2010

Three-dimensional object recognition is viewpoint dependent

Effect of computer-aided detection for CT colonography in a multireader, multicase trial.

A fully automatic CAD-CTC system based on curvature analysis for standard and low-dose CT data.

Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data.

Increasing computer-aided detection specificity by projection features for CT colonography

Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning.

Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.

Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography

Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population

P304

1141-1153

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1109/TMI.2012.2187304

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Berkman Sahiner

P577

2012-05-01T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

224888108

http://www.w3.org/2001/XMLSchema#string

P698

22552333

http://www.w3.org/2001/XMLSchema#string

P932

3480731

http://www.w3.org/2001/XMLSchema#string

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

about

P2860

P2860

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P50

P577

P5875

P698

P932

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P50

P577

P5875

P698

P932