TY - GEN
T1 - Dense Encoder-Decoder Network based on Two-Level Context Enhanced Residual Attention Mechanism for Segmentation of Breast Tumors in Magnetic Resonance Imaging
AU - Gao, Ying
AU - Zhao, Yin
AU - Luo, Xiongwen
AU - Hu, Xiping
AU - Liang, Changhong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Aiming to effective early detection of breast cancer, automatic tumor segmentation based on breast Magnetic Resonance Imaging (MRI) is concentrated by more and more researchers. This paper proposes a dense encoder-decoder network based on two-level context enhanced residual attention mechanism (TLCRAM-DED). With respect to TLCRAM-DED, we design the encoding structure combining two-level residual attention structure with dense block to extract and refine the features of different layers. Meanwhile, a dense multi-scale atrous convolution is used at the end of the encoder to obtain a larger receptive field and enrich the extracted semantic information. Moreover, residual attention structure (RAS) is also used for the refinement during decoding stage, while a long connection formed with the encoder RAS output is applied to supplement the features and to gradually recover the segmentation details. We validated prosed model in the DCE sequence of challenging breast cancer MRI dataset. The average Dice coefficient is up to 81.04%, which outperforms compared state-of-the-arts.
AB - Aiming to effective early detection of breast cancer, automatic tumor segmentation based on breast Magnetic Resonance Imaging (MRI) is concentrated by more and more researchers. This paper proposes a dense encoder-decoder network based on two-level context enhanced residual attention mechanism (TLCRAM-DED). With respect to TLCRAM-DED, we design the encoding structure combining two-level residual attention structure with dense block to extract and refine the features of different layers. Meanwhile, a dense multi-scale atrous convolution is used at the end of the encoder to obtain a larger receptive field and enrich the extracted semantic information. Moreover, residual attention structure (RAS) is also used for the refinement during decoding stage, while a long connection formed with the encoder RAS output is applied to supplement the features and to gradually recover the segmentation details. We validated prosed model in the DCE sequence of challenging breast cancer MRI dataset. The average Dice coefficient is up to 81.04%, which outperforms compared state-of-the-arts.
KW - Deep learning
KW - breast tumor segmentation
KW - dense encoder-decoder network
KW - multi-scale atrous convolution
KW - residual attention structure
UR - http://www.scopus.com/inward/record.url?scp=85084333666&partnerID=8YFLogxK
U2 - 10.1109/BIBM47256.2019.8983316
DO - 10.1109/BIBM47256.2019.8983316
M3 - Conference contribution
AN - SCOPUS:85084333666
T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
SP - 1123
EP - 1129
BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
A2 - Yoo, Illhoi
A2 - Bi, Jinbo
A2 - Hu, Xiaohua Tony
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Y2 - 18 November 2019 through 21 November 2019
ER -
