Improving breast cancer diagnosis with computer-aided diagnosis

TL;DR: Use of different types of reporting data in the computation of sensitivity and specificity may result in different conclusions concerning the benefit of computer-aided diagnosis, as well as the use of user-dependent thresholds.

TL;DR: A potential clinical role of CAD in mammography for the detection of breast cancer is indicated by reviewing several laboratory observer performance studies of computer-aided diagnosis of malignant and benign breast lesions.

TL;DR: Use of additional cases from a reference library that have similar image features can improve the classification accuracy of a CADx classifier on a query case and can even outperform retraining the classifier with all the cases from the entire reference library.

TL;DR: The results indicate that with uniform background and no magnification, the FP system performed the best while the SF system did slightly better than the CCD system, and with magnification added, all detection tasks were improved except for the smallest and largest one or two size groups.

TL;DR: In this article, several methods of estimating error rates in discriminant analysis are evaluated by sampling methods, and two methods in most common use are found to be significantly poorer than some new methods that are proposed.

TL;DR: The effects of sample size on feature selection and error estimation for several types of classifiers are discussed and an emphasis is placed on giving practical advice to designers and users of statistical pattern recognition systems.

TL;DR: Two new algorithms for fitting binormal ROC curves to continuously-distributed data are developed: a true ML algorithm (LABROC4) and a quasi-ML algorithm (LabROC5) that requires substantially less computation with large data sets.