ST-Double-Net: A Two-Stage Breast Tumor Classification Model Based on Swin Transformer and Weakly Supervised Target Localization-Reference-Cited by-同舟云学术

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ST-Double-Net: A Two-Stage Breast Tumor Classification Model Based on Swin Transformer and Weakly Supervised Target Localization

Published:2024 Issue: Volume:12 Page:117921-117933
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hao Shengnan¹,Jia Yihan¹,Liu Jianuo¹,Wang Zhiwu²^ORCID,Liu Chunling²,Ji Zhanlin³^ORCID,Ganchev Ivan⁴^ORCID

Affiliation:

1. Department of Artificial Intelligence, North China University of Science and Technology, Tangshan, China

2. Hebei Key Laboratory of Molecular Oncology, Tangshan, Hebei, China

3. College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou, China

4. Telecommunications Research Centre (TRC), University of Limerick, Limerick, Ireland

Funder

National Key Research and Development Program of China

Bulgarian National Science Fund

Telecommunications Research Centre (TRC) of University of Limerick, Ireland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10639378.pdf?arnumber=10639378

Reference50 articles.

1. Research on classification method of mammography based on deep learning;Sun;Comput. Eng. Appl.,2018

2. Breast MRI: State of the Art

3. Level Set Contouring for Breast Tumor in Sonography

4. Calcifications at Digital Breast Tomosynthesis: Imaging Features and Biopsy Techniques

5. Deep Residual Learning for Image Recognition

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