期刊
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2015, PT I
卷 9349, 期 -, 页码 605-612出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-24553-9_74
关键词
Deep learning; Structured output learning; Mammogram segmentation
In this paper, we explore the use of deep convolution and deep belief networks as potential functions in structured prediction models for the segmentation of breast masses from mammograms. In particular, the structured prediction models are estimated with loss minimization parameter learning algorithms, representing: a) conditional random field (CRF), and b) structured support vector machine (SSVM). For the CRF model, we use the inference algorithm based on tree re-weighted belief propagation with truncated fitting training, and for the SSVM model the inference is based on graph cuts with maximum margin training. We show empirically the importance of deep learning methods in producing state-of-the-art results for both structured prediction models. In addition, we show that our methods produce results that can be considered the best results to date on DDSM-BCRP and INbreast databases. Finally, we show that the CRF model is significantly faster than SSVM, both in terms of inference and training time, which suggests an advantage of CRF models when combined with deep learning potential functions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据