Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings.

Purpose: Computer-aided detection (CADe) systems for mammography screening still mark many false positives. This can cause radiologists to lose confidence in CADe, especially when many false positives are obviously not suspicious to them. In this study, we focus on obvious false positives generated by microcalcification detection algorithms.

Methods: We aim at reducing the number of obvious false-positive findings by adding an additional step in the detection method. In this step, a multiclass machine learning method is implemented in which dedicated classifiers learn to recognize the patterns of obvious false-positive subtypes that occur most frequently. The method is compared to a conventional two-class approach, where all false-positive subtypes are grouped together in one class, and to the baseline CADe system without the new false-positive removal step. The methods are evaluated on an independent dataset containing 1,542 screening examinations of which 80 examinations contain malignant microcalcifications.

Results: Analysis showed that the multiclass approach yielded a significantly higher sensitivity compared to the other two methods (P < 0.0002). At one obvious false positive per 100 images, the baseline CADe system detected 61% of the malignant examinations, while the systems with the two-class and multiclass false-positive reduction step detected 73% and 83%, respectively.

Conclusions: Our study showed that by adding the proposed method to a CADe system, the number of obvious false positives can decrease significantly (P < 0.0002).