Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a conditional random field (CRF) to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Further, we employ adversarial training to eliminate over-fitting due to the small sizes of mammogram datasets. Multi-scale FCN is employed to improve the segmentation performance. Experimental results on two public datasets, INbreast and DDSM-BCRP, demonstrate that our end-to-end network achieves better performance than state-of-the-art approaches. 1
Zhu et al. REF propose an unified end-to-end adversarial training network integrating FCN and Conditional Random Fields (CRF), which is able to reduce over-fitting caused by small datasets.
Adversarial deep structured nets for mass segmentation from mammograms
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Computer Science