  Background and Objective: Accurate detection of breast masses in mammography
images is critical to diagnose early breast cancer, which can greatly improve
the patients survival rate. However, it is still a big challenge due to the
heterogeneity of breast masses and the complexity of their surrounding
environment.Methods: To address these problems, we propose a one-stage object
detection architecture, called Breast Mass Detection Network (BMassDNet), based
on anchor-free and feature pyramid which makes the detection of breast masses
of different sizes well adapted. We introduce a truncation normalization method
and combine it with adaptive histogram equalization to enhance the contrast
between the breast mass and the surrounding environment. Meanwhile, to solve
the overfitting problem caused by small data size, we propose a natural
deformation data augmentation method and mend the train data dynamic updating
method based on the data complexity to effectively utilize the limited data.
Finally, we use transfer learning to assist the training process and to improve
the robustness of the model ulteriorly.Results: On the INbreast dataset, each
image has an average of 0.495 false positives whilst the recall rate is 0.930;
On the DDSM dataset, when each image has 0.599 false positives, the recall rate
reaches 0.943.Conclusions: The experimental results on datasets INbreast and
DDSM show that the proposed BMassDNet can obtain competitive detection
performance over the current top ranked methods.
