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Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation
Joint Authors
Shi, Dapeng
Zeng, Lei
Chen, Jian
Qiao, Kai
Hai, Jinjin
Xu, Jingbo
Tan, Hongna
Yan, Bin
Source
Journal of Healthcare Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates.
In this paper, we propose a fully convolutional network to achieve automatic segmentation of breast tumor in an end-to-end manner.
Considering the diversity of shape and size for malignant tumors in the digital mammograms, we introduce multiscale image information into the fully convolutional dense network architecture to improve the segmentation precision.
Multiple sampling rates of atrous convolution are concatenated to acquire different field-of-views of image features without adding additional number of parameters to avoid over fitting.
Weighted loss function is also employed during training according to the proportion of the tumor pixels in the entire image, in order to weaken unbalanced classes problem.
Qualitative and quantitative comparisons demonstrate that the proposed algorithm can achieve automatic tumor segmentation and has high segmentation precision for various size and shapes of tumor images without preprocessing and postprocessing.
American Psychological Association (APA)
Hai, Jinjin& Qiao, Kai& Chen, Jian& Tan, Hongna& Xu, Jingbo& Zeng, Lei…[et al.]. 2019. Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1175385
Modern Language Association (MLA)
Hai, Jinjin…[et al.]. Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation. Journal of Healthcare Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1175385
American Medical Association (AMA)
Hai, Jinjin& Qiao, Kai& Chen, Jian& Tan, Hongna& Xu, Jingbo& Zeng, Lei…[et al.]. Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1175385
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175385