The effect of data set size on computer-aided diagnosis of breast cancer: comparing decision fusion to a linear discriminant

Jonathan L. Jesneck; Loren W. Nolte; Jay A. Baker M.D.; Joseph Y. Lo

doi:10.1117/12.655235

17 March 2006 The effect of data set size on computer-aided diagnosis of breast cancer: comparing decision fusion to a linear discriminant

Jonathan L. Jesneck, Loren W. Nolte, Jay A. Baker M.D., Joseph Y. Lo

Author Affiliations +

Proceedings Volume 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment; 614616 (2006) https://doi.org/10.1117/12.655235
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

Data sets with relatively few observations (cases) in medical research are common, especially if the data are expensive or difficult to collect. Such small sample sizes usually do not provide enough information for computer models to learn data patterns well enough for good prediction and generalization. As a model that may be able to maintain good classification performance in the presence of limited data, we used decision fusion. In this study, we investigated the effect of sample size on the generalization ability of both linear discriminant analysis (LDA) and decision fusion. Subsets of large data sets were selected by a bootstrap sampling method, which allowed us to estimate the mean and standard deviation of the classification performance as a function of data set size. We applied the models to two breast cancer data sets and compared the models using receiver operating characteristic (ROC) analysis. For the more challenging calcification data set, decision fusion reached its maximum classification performance of AUC = 0.80±0.04 at 50 samples and pAUC = 0.34±0.05 at 100 samples. The LDA reached a lower performance and required many more cases, with a maximum of AUC = 0.68±0.04 and pAUC = 0.12±0.05 at 450 samples. For the mass data set, the two classifiers had more similar performance, with AUC = 0.92±0.02 and pAUC = 0.48±0.02 at 50 samples for decision fusion and AUC = 0.92±0.03 and pAUC = 0.55±0.04 at 500 samples for the LDA.

Citation Download Citation

Jonathan L. Jesneck, Loren W. Nolte, Jay A. Baker M.D., and Joseph Y. Lo "The effect of data set size on computer-aided diagnosis of breast cancer: comparing decision fusion to a linear discriminant", Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 614616 (17 March 2006); https://doi.org/10.1117/12.655235

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

Includes PDF, HTML & Video, when available