Preliminary investigation on CAD system update: effect of selection of new cases on classifier performance

Chisako Muramatsu; Kohei Nishimura; Takeshi Hara; Hiroshi Fujita

doi:10.1117/12.2007355

28 February 2013 Preliminary investigation on CAD system update: effect of selection of new cases on classifier performance

Chisako Muramatsu, Kohei Nishimura, Takeshi Hara, Hiroshi Fujita

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86701T (2013) https://doi.org/10.1117/12.2007355
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

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.

Citation Download Citation

Chisako Muramatsu, Kohei Nishimura, Takeshi Hara, and 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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE