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• W2155487617 abstract "In this paper, we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs). The classifier is part of a computer-aided diagnosis (CADx) scheme that is aimed to assisting radiologists in making more accurate diagnoses of breast cancer on mammograms. The methods we considered were: support vector machine (SVM), kernel Fisher discriminant (KFD), relevance vector machine (RVM), and committee machines (ensemble averaging and AdaBoost), of which most have been developed recently in statistical learning theory. We formulated differentiation of malignant from benign MCs as a supervised learning problem, and applied these learning methods to develop the classification algorithm. As input, these methods used image features automatically extracted from clustered MCs. We tested these methods using a database of 697 clinical mammograms from 386 cases, which included a wide spectrum of difficult-to-classify cases. We analyzed the distribution of the cases in this database using the multidimensional scaling technique, which reveals that in the feature space the malignant cases are not trivially separable from the benign ones. We used receiver operating characteristic (ROC) analysis to evaluate and to compare classification performance by the different methods. In addition, we also investigated how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD, and RVM) yielded the best performance (Az = 0.85, SVM), significantly outperforming a well-established, clinically-proven CADx approach that is based on neural network (Az = 0.80)." @default.
• W2155487617 created "2016-06-24" @default.
• W2155487617 creator A5027400966 @default.
• W2155487617 creator A5042975544 @default.
• W2155487617 creator A5048510347 @default.
• W2155487617 creator A5079423135 @default.
• W2155487617 date "2005-03-01" @default.
• W2155487617 modified "2023-09-30" @default.
• W2155487617 title "A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications" @default.
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• W2155487617 doi "https://doi.org/10.1109/tmi.2004.842457" @default.
• W2155487617 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15754987" @default.
• W2155487617 hasPublicationYear "2005" @default.
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