Breast Cancer Image Semantic Segmentation with Attention U-Net

Authors

  • Myung-Jae Lim Department of Medical IT, Eulji University, SeongNam city, Korea
  • Jeong-Eun Kim Department of Medical IT, Eulji University, SeongNam city, Korea
  • Young-Chae Kim Department of Medical IT, Eulji University, SeongNam city, Korea
  • Dong-Keun Chung Department of Medical IT, Eulji University, SeongNam city, Korea
  • Kyu-Ho Kim Department of Medical IT, Eulji University, SeongNam city, Korea

DOI:

https://doi.org/10.15379/ijmst.v10i1.1452

Keywords:

Semantic Segmentation, U-net, Soft Attention, Attention gate, Sigmoid, Decoder

Abstract

Semantic segmentation is to segment objects in an image into meaningful units. Among them, the basic idea of U-Net is to use low-dimensional as well as high-dimensional information to extract image features and enable accurate location identification. In this paper, we present a new model that combines Attention Gates with U-Net and evaluate the results through semantic segmentation with breast cancer datasets. To this end, this study proposes and tests a methodology for breast cancer image segmentation based on Attention U-Net. In conclusion, when comparing the performance with the existing U-Net, It can be seen that IoU is 0.069 higher than the existing U-Net. Thus, the proposed model enables better image semantic segmentation.

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Published

2023-06-01

How to Cite

[1]
M.-J. . Lim, J.-E. . Kim, Y.-C. . Kim, D.-K. . Chung, and K.-H. . Kim, “Breast Cancer Image Semantic Segmentation with Attention U-Net”, ijmst, vol. 10, no. 1, pp. 249-253, Jun. 2023.