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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Published in:	IEEE transactions on biomedical engineering Vol. 55; no. 3; pp. 1015 - 1021
Main Authors:	Dundar, M. Murat, Fung, Glenn, Krishnapuram, Balaji, Rao, R. Bharat
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.03.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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Abstract	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 expensive for these datasets. We describe CH, a framework for learning a convex hull representation of multiple instances that is significantly faster than existing MIL algorithms. Our CH framework applies to any standard hyperplane-based learning algorithm, and for some algorithms, is guaranteed to find the global optimal solution. Experimental studies on two different CAD applications further demonstrate that the proposed algorithm significantly improves diagnostic accuracy when compared to both MIL and traditional classifiers. Although not designed for standard MIL problems (which have both positive and negative bags and relatively balanced datasets), comparisons against other MIL methods on benchmark problems also indicate that the proposed method is competitive with the state-of-the-art.
AbstractList	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 expensive for these datasets. We describe CH, a framework for learning a Convex Hull representation of multiple instances that is significantly faster than existing MIL algorithms. Our CH framework applies to any standard hyperplane-based learning algorithm, and for some algorithms, is guaranteed to find the global optimal solution. Experimental studies on two different CAD applications further demonstrate that the proposed algorithm significantly improves diagnostic accuracy when compared to both MIL and traditional classifiers. Although not designed for standard MIL problems (which have both positive and negative bags and relatively balanced datasets), comparisons against other MIL methods on benchmark problems also indicate that the proposed method is competitive with the state-of-the-art.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 expensive for these datasets. We describe CH, a framework for learning a Convex Hull representation of multiple instances that is significantly faster than existing MIL algorithms. Our CH framework applies to any standard hyperplane-based learning algorithm, and for some algorithms, is guaranteed to find the global optimal solution. Experimental studies on two different CAD applications further demonstrate that the proposed algorithm significantly improves diagnostic accuracy when compared to both MIL and traditional classifiers. Although not designed for standard MIL problems (which have both positive and negative bags and relatively balanced datasets), comparisons against other MIL methods on benchmark problems also indicate that the proposed method is competitive with the state-of-the-art. 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 expensive for these datasets. We describe CH, a framework for learning a convex hull representation of multiple instances that is significantly faster than existing MIL algorithms. Our CH framework applies to any standard hyperplane-based learning algorithm, and for some algorithms, is guaranteed to find the global optimal solution. Experimental studies on two different CAD applications further demonstrate that the proposed algorithm significantly improves diagnostic accuracy when compared to both MIL and traditional classifiers. Although not designed for standard MIL problems (which have both positive and negative bags and relatively balanced datasets), comparisons against other MIL methods on benchmark problems also indicate that the proposed method is competitive with the state-of-the-art. 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. Many computer-aided diagnosis (CAD) problems can be best modelled as a multiple-instance learning (MIL) problem with unbalanced data, i.o., 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 expensive for these datasets. We describe CH, a framework for learning a convex huil representation of multiple instances that is significantly taster than existing MIL algorithms. Our CH framework apples to any standard hyperplane-based barring algorithm, and for some algorithms, is guaranteed to find the global optimal solution. Experimental studies on two different CAD applications further demonstrate that the proposed algorithm significantly improves diagnostic accuracy when compared to both MIL and traditional classifiers. Although not designed for standard MIL problems (which have both positive and negative bags and relatively balanced datasets), comparisons against ofter MIL methods on benchmark problems also indicate that the proposed method is competitive with the state-of-the-art. 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 expensive for these datasets. We describe CH, a framework for learning a convex hull representaion of multiple instances that is significantly faster than existing MIL algorithms. Our CH framework applies to any standard hyperpiane-based learning algorithm, and for some algorithms, is guaranteed to find the global optimal solution. Experimental studies on two different CAD applications further demonstrate that the proposed algorithm significantly improves diagnostic accuracy when compared to both MIL and traditional classifiers. Although not designed for standard MIL problems (which have both positive and negative bags and relatively balanced datasets), comparisons against other MIL methods on benchmark problems also indicate that the proposed method is competitive with the state-of-the-art.
Author	Krishnapuram, Balaji Rao, R. Bharat Dundar, M. Murat Fung, Glenn
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References	mika (ref11) 2000; 12 ref15 zhou (ref18) 2003; 2837 ref14 mangasarian (ref8) 2005 mangasarian (ref9) 2000 lee (ref7) 2000 bezdek (ref2) 2003; 11 ref17 jerebko (ref6) 2006; 2837 maron (ref10) 1998; 10 ref4 ref3 andrews (ref1) 2003 zhang (ref16) 2001; 14 ref5 quist (ref12) 2004 ramon (ref13) 2000
References_xml	– volume: 14 start-page: 1073 year: 2001 ident: ref16 publication-title: Advances in neural information processing systems – year: 2000 ident: ref7 publication-title: RSVM Reduced support vector machines – ident: ref17 doi: 10.1117/12.481369 – volume: 2837 start-page: 492 year: 2003 ident: ref18 article-title: ensembles of multi-instance learners publication-title: Proc 14th Eur Conf Mach Learning – ident: ref14 doi: 10.1007/978-1-4757-2440-0 – volume: 10 year: 1998 ident: ref10 publication-title: Advances in Neural Information Processing Systems 10 – ident: ref15 doi: 10.1137/0111043 – start-page: 561 year: 2003 ident: ref1 publication-title: Advances in Neural Information Processing Systems 15 – volume: 12 start-page: 591 year: 2000 ident: ref11 publication-title: Advances in neural information processing systems – year: 2000 ident: ref13 article-title: multi instance neural networks publication-title: Proc ICML-2000 Workshop Attribute-Value and Relational Learning – volume: 11 start-page: 351 year: 2003 ident: ref2 article-title: convergence of alternating optimization publication-title: Neural Parallel Sci Comput – ident: ref3 doi: 10.1259/bjr/25777270 – ident: ref4 doi: 10.1016/S0004-3702(96)00034-3 – ident: ref5 doi: 10.3322/canjclin.54.1.8 – year: 2004 ident: ref12 article-title: computer-aided detection of pulmonary embolism on multi-detector ct publication-title: Proce 90th Meeting Radiol Soc North America (RSNA) – volume: 2837 start-page: 169 year: 2006 ident: ref6 article-title: symmetric curvature patterns for colonic polyp detection publication-title: Proc 14th Eur Conf Mach Learning – year: 2005 ident: ref8 publication-title: Multiple instance classification via successive linear programming – start-page: 135 year: 2000 ident: ref9 publication-title: Advances in Large Margin Classifiers doi: 10.7551/mitpress/1113.003.0012
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SubjectTerms	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
Title	Multiple-Instance Learning Algorithms for Computer-Aided Detection
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