Multiple-Instance Learning Algorithms for Computer-Aided Detection

Many computer-aided diagnosis (CAD) problems can be best modelled as a multiple-instance learning (MIL) problem with unbalanced data, i.e., the training data typically consists of a few positive bags, and a very large number of negative instances. Existing MIL algorithms are much too computationally...

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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 55; H. 3; S. 1015 - 1021
Hauptverfasser:	Dundar, M. Murat, Fung, Glenn, Krishnapuram, Balaji, Rao, R. Bharat
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Alternate optimization Artificial Intelligence Biomedical imaging Colonic Neoplasms - diagnostic imaging Computed tomography convex hull Design automation Fisher discriminant Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Labeling Magnetic resonance imaging Medical diagnostic imaging multiple-instance learning (MIL) Pattern Recognition, Automated - methods Pulmonary Embolism - diagnostic imaging Radiography Reproducibility of Results Sensitivity and Specificity Studies Training data X-ray detection X-ray detectors X-ray imaging fisher discriminant alternate optimization convex hull multiple instance learning
ISSN:	0018-9294, 1558-2531
Online-Zugang:	Volltext
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