Breast Lesion Segmentation in Ultrasound Images by CDeep3M
Shida Wang, Jin Huang · 2020 International Conference on Computer Engineering and Application (ICCEA) · 2020
The proper segmentation of breast lesion based on mammography is crucial not only for traditional computer aided diagnosis but also for supervised learning by deep learning, which is significant for further quantitative analysis. To achieve a comprehensive diagnosis with versatile deep learning algorithms, a novel Docker based framework is proposed and designed by incorporating popular CNN based models CDeep3M. The validation is preceded under a unique amount of B-type ultrasound images. The results indicate that CDeep3M have a good capability for breast tumor prediction. Besides, the analysis shows that Docker framework is flexible and extensive for environmental construction for breast cancer real-time prediction, as well as other related applications.