Abstract
BackgroundBreast cancer continues to be a leading cause of cancer deaths among women, especially in Western countries. In the last two decades, many methods have been proposed to achieve a robust mammography‐based computer aided detection (CAD) system. A CAD system should provide high performance over time and in different clinical situations. I.e., the system should be adaptable to different clinical situations and should provide consistent performance.MethodsWe tested our system seeking a measure of the guarantee of its consistent performance. The method is based on blind feature extraction by independent component analysis (ICA) and classification by neural networks (NN) or SVM classifiers. The test mammograms were from the Digital Database for Screening Mammography (DDSM). This database was constructed collaboratively by four institutions over more than 10 years. We took advantage of this to train our system using the mammograms from each institution separately, and then testing it on the remaining mammograms. We performed another experiment to compare the results and thus obtain the measure sought. This experiment consists in to form the learning sets with all available prototypes regardless of the institution in which them were generated, obtaining in that way the overall results.ResultsThe smallest variation from comparing the results of the testing set in each experiment (performed by training the system using the mammograms from one institution and testing with the remaining) with those of the overall result, considering the success rate for an intermediate decision maker threshold, was roughly 5%, and the largest variation was roughly 17%. But, if we considere the area under ROC curve, the smallest variation was close to 4%, and the largest variation was about a 6%.ConclusionsConsidering the heterogeneity in the datasets used to train and test our system in each case, we think that the variation of performance obtained when the results are compared with the overall results is acceptable in both cases, for NN and SVM classifiers. The present method is therefore very general in that it is able to adapt to different clinical situations and provide consistent performance.
Highlights
Breast cancer continues to be a leading cause of cancer deaths among women, especially in Western countries
This is because for this choice there are far more prototypes in the learning set than in the test set (Table 1), and the classifier trained with this distribution can “learn” prototypes from all the scanners, while, those trained with the other distributions can only learn prototypes from one scanner
As was noted above, in theory, the scanner used for scanning should not affect the final results because the prototype images are transformed to optical densities using the scanner calibration parameters provided by the Digital Database for Screening Mammography (DDSM)’s authors, but yes the different styles used for indicating the lesions on the mammograms and, the number of prototypes in the learning set in each case
Summary
Breast cancer continues to be a leading cause of cancer deaths among women, especially in Western countries. As was observed in [1], to detect and diagnose lesions (mainly masses and microcalcifications) in mammograms, a CAD system needs to satisfy various quality criteria: high sensitivity to detect the greatest possible number of cancers; high specificity to reduce the frequency of false positives per image; acceptable call rate; early detection to increase the patient’s chances of survival; fast processing time; and robustness. This last is in the sense that the system should be adaptable to different clinical situations and should provide consistent performance. It is usually difficult to compare the results of different studies addressing both the detection and diagnosis of masses, the main problem being either the use of smallsize proprietary databases or, if they use a public database, the use of selected, unspecified cases
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