  Microcalcifications are small deposits of calcium that appear in mammograms
as bright white specks on the soft tissue background of the breast.
Microcalcifications may be a unique indication for Ductal Carcinoma in Situ
breast cancer, and therefore their accurate detection is crucial for diagnosis
and screening. Manual detection of these tiny calcium residues in mammograms is
both time-consuming and error-prone, even for expert radiologists, since these
microcalcifications are small and can be easily missed. Existing computerized
algorithms for detecting and segmenting microcalcifications tend to suffer from
a high false-positive rate, hindering their widespread use. In this paper, we
propose an accurate calcification segmentation method using deep learning. We
specifically address the challenge of keeping the false positive rate low by
suggesting a strategy for focusing the hard pixels in the training phase.
Furthermore, our accurate segmentation enables extracting meaningful statistics
on clusters of microcalcifications.
