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Papers Published

1. Markey, M.K. and Lo, J.Y. and Floyd, C.E., Jr., Differences between computer-aided diagnosis of breast masses and that of calcifications, Radiology (USA), vol. 223 no. 2 (2002), pp. 489 - 93 .
   (last updated on 2007/04/15)

   Abstract:
   The authors compared the performance of a computer-aided diagnosis (CAD) system for diagnosis of previously detected lesions, based on radiologist-extracted findings on masses and calcifications. A feed-forward, back-propagation artificial neural network (BP-ANN) was trained in a round-robin (leave-one-out) manner to predict biopsy outcome from mammographic findings (according to the Breast Imaging Reporting and Data System) and patient age. The BP-ANN was trained by using a large (>1,000 cases) heterogeneous data set containing masses and microcalcifications. The performances of the BP-ANN on masses and microcalcifications were compared with use of receiver operating characteristic analysis and a z test for uncorrelated samples. The BP-ANN performed significantly better on masses than microcalcifications in terms of both the area under the receiver operating characteristic curve and the partial receiver operating characteristic area index. A similar difference in performance was observed with a second model (linear discriminant analysis) and also with a second data set from similar institution. In conclusion, masses and calcifications should be considered separately when evaluating CAD systems for breast cancer diagnosis

   Keywords:
   cancer;feedforward neural nets;mammography;medical image processing;