BUSnet: A Deep Learning Model of Breast Tumor Lesion Detection for Ultrasound Images - Li, Yujie; Gu, Hong; Wang, Hongyu; Qin, Pan; Wang, Jia (Frontiers in Oncology, 2022)
Ultrasound (US) imaging is a main modality for breast disease screening. Automatically detecting the lesions in US images is essential for developing the artificial-intelligence-based diagnostic support technologies. However, the intrinsic characteristics of ultrasound imaging, like speckle noise and acoustic shadow, always degenerate the detection accuracy. In this study, we developed a deep learning model called BUSnet to detect the breast tumor lesions in US imagines with high accuracy. We first developed a two-stage method including the unsupervised region proposal and bounding-box regression algorithms. Then, we proposed a post-processing method to enhance the detecting accuracy further. The proposed method was used to a benchmark dataset, { which includes 487 benign samples and 210 malignant samples.} The results proved the effectiveness and accuracy of the proposed method.