You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
28 February 2013Preliminary investigation on CAD system update: effect of selection of new cases on classifier performance
When a computer-aided diagnosis (CAD) system is used in clinical practice, it is desirable that the system is constantly
and automatically updated with new cases obtained for performance improvement. In this study, the effect of different
case selection methods for the system updates was investigated. For the simulation, the data for classification of benign and malignant masses on mammograms were used. Six image features were used for training three classifiers: linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbors (kNN). Three datasets, including dataset I for initial training of the classifiers, dataset T for intermediate testing and retraining, and dataset E for evaluating the classifiers, were randomly sampled from the database. As a result of intermediate testing, some cases from dataset T were selected to be added to the previous training set in the classifier updates. In each update, cases were selected using 4 methods: selection of (a) correctly classified samples, (b) incorrectly classified samples, (c) marginally classified samples, and (d) random samples. For comparison, system updates using all samples in dataset T were also evaluated. In general, the average areas under the receiver operating characteristic curves (AUCs) were almost unchanged with method (a), whereas AUCs generally degraded with method (b). The AUCs were improved with method (c) and (d), although use of all available cases generally provided the best or nearly best AUCs. In conclusion, CAD systems may be improved by retraining with new cases accumulated during practice.
The alert did not successfully save. Please try again later.
Chisako Muramatsu, Kohei Nishimura, Takeshi Hara, Hiroshi Fujita, "Preliminary investigation on CAD system update: effect of selection of new cases on classifier performance," Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701T (28 February 2013); https://doi.org/10.1117/12.2007355