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 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.
Dundar
M Murat
MM
Computer Aided Diagnosis and Knowledge Solutions, Siemens Medical Solutions, 51 Valley Stream Parkway, MS E51, Malvern, PA 19355, USA. murat.dundar@siemens.com
Fung
Glenn
G
Krishnapuram
Balaji
B
Rao
R Bharat
RB
D000465:Algorithms; D001185:Artificial Intelligence; D003110:Colonic Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging*; D011859:Radiography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity