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Rapid and precise segmentation of breast tumors is a severe challenge for the global research community to diagnose breast cancer in younger females. An ultrasound system is a non-invasive and efficient way of breast screening. The area, shape, and texture of different breast tumors play a vital role for clinicians in making accurate diagnostic decisions. Furthermore, the limited availability of breast tumor annotated datasets is another challenge for properly training deep neural networks. This research proposes a semi-supervised learning-based method, which incorporates a Data expansion network (DEN), Probability map generator network (PMG), and U-shaped pyramid-dilated fusion network (PDF-UNet) for accurate breast tumor segmentation. The first DEN network is trained on breast unannotated tumor images and generates synthetic images for the data expansion task. The second PMG network generates …