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ECR 2019 / C-2062
Representation learning approach to breast cancer diagnosis
Congress: ECR 2019
Poster No.: C-2062
Type: Scientific Exhibit
Keywords: Breast, Artificial Intelligence, Computer applications, Mammography, Computer Applications-Detection, diagnosis, Diagnostic procedure, Decision analysis, Cancer, Image verification
Authors: J. P. Pereira Fontes, M. A. Guevara Lopez; Guimarães/PT
DOI:10.26044/ecr2019/C-2062

Conclusion

When compared with the base approach our new proposed method improves the classifier AUC from 0.826 to 0.978. The best approach uses the DL model for feature representation learning, in this case, the penultimate layer from the DL model, to feed a Random Forest classifier that enables, in average, to correctly classify 97.93% of the test data (BCDR-F03 dataset). The test in FFDM showed surprising results, giving us an accuracy of over 75% in all classifiers. This fact was significant since the existing samples of FFDM are not sufficiently representative to allow the creation of a dedicated classifier. Thus, the developed system is very promissory and could be used as a second opinion, in clinical workflows, producing acceptable results, although we consider that they have still not achieved the desired level of accuracy.

Finally, this is one more result that demonstrates that feature representation learning techniques are currently a good way to improve the performance of breast cancer CADx systems.

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