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Breast cancer is one of the global leading causes of death among women, and an early
detection is of uttermost importance to reduce mortality rates. Screening mammograms,
in which radiologists rely only on their eyesight, are one of the most used early detection
methods. However, characteristics, such as the asymmetry between breasts, a feature
that could be very difficult to visually quantize, is key to breast cancer detection. Due
to the highly heterogeneous and deformable structure of the breast itself, incorporating
asymmetry measurements into an automated detection system is still a challenge.
In this study, we proposed the use of a bilateral registration algorithm as an effective
way to automatically measure mirror asymmetry. Furthermore, this information was fed
to a machine learning algorithm to improve the accuracy of the model. In this study,
449 subjects (197 with calcifications, 207 with masses, and 45 healthy subjects) from a
public database were used to train and evaluate the proposed methodology. Using this
procedure, we were able to independently identify subjects with calcifications (accuracy
= 0.825, AUC = 0.882) and masses (accuracy = 0.698, AUC = 0.807) from healthy subjects
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