  Breast cancer is one of the factors that cause the increase of mortality of
women. The most widely used method for diagnosing this geological disease i.e.
breast cancer is the ultrasound scan. Several key features such as the
smoothness and the texture of the tumor captured through ultrasound scans
encode the abnormality of the breast tumors (malignant from benign). However,
ultrasound scans are often noisy and include irrelevant parts of the breast
that may bias the segmentation of eventual tumors. In this paper, we are going
to extract the region of interest ( i.e, bounding boxes of the tumors) and
feed-forward them to one semantic segmentation encoder-decoder structure based
on its classification (i.e, malignant or benign). the whole process aims to
build an instance-based segmenter from a semantic segmenter and an object
detector.
