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Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.
Li, Yuan-Zhe; Huang, Yin-Hui; Su, Xian-Yan; Gu, Zhen-Qi; Lai, Qing-Quan; Huang, Jing; Li, Shu-Ting; Wang, Yi.
Affiliation
  • Li YZ; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
  • Huang YH; Department of Neurology, Jinjiang Municipal Hospital, Quanzhou 362000, China.
  • Su XY; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
  • Gu ZQ; Galactophore Department, The First School of Clinical Medicine, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou 310000, China.
  • Lai QQ; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
  • Huang J; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
  • Li ST; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
  • Wang Y; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
Comput Math Methods Med ; 2022: 1770531, 2022.
Article in En | MEDLINE | ID: mdl-36238476
ABSTRACT

Results:

The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors.

Conclusion:

Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Neoplasms / Deep Learning Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans Language: En Journal: Comput Math Methods Med Journal subject: INFORMATICA MEDICA Year: 2022 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Neoplasms / Deep Learning Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans Language: En Journal: Comput Math Methods Med Journal subject: INFORMATICA MEDICA Year: 2022 Document type: Article Affiliation country: China