Young Researcher Paper Award 2023
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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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English 日本語
Instructions for manuscript preparation
English 日本語
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MYU K.K.
Sensors and Materials
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The Art of Writing Scientific Papers
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 36, Number 5(2) (2024)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1951-1957
S&M3646 Research Paper of Special Issue https://doi.org/10.18494/SAM4826 Published: May 24, 2024 Mammographic Breast Composition Classification Using Swin Transformer Network [PDF] Kuen-Jang Tsai, Wei-Cheng Yeh, Cheng-Yi Kao, Ming-Wei Lin, Chao-Ming Hung, Hung-Ying Chi, Cheng-Yu Yeh, and Shaw-Hwa Hwang (Received December 15, 2023; Accepted May 10, 2024) Keywords: screening mammography, breast imaging reporting and data system (BI-RADS), breast composition, image classification, Swin Transformer, deep learning
Breast cancer is a prevalent global health concern and the most commonly diagnosed cancer in women. Mammography, a well-established and widely used screening tool, has greatly contributed to early breast cancer detection. However, understanding mammographic breast composition is also crucial for refining the risk assessment of breast cancer beyond identifying lesions. In contrast to previous studies, we adopt an exploratory approach by using the Swin Transformer, a foundation model for image classification, to classify the four-category breast density. Leveraging this foundation, we fine-tune the model with a small set of mammograms for the purpose of making advancements in breast density classification. This study is experimentally validated to achieve an overall accuracy of 74.96% in the four-category breast density classification, which is a comparable performance to recent counterparts.
Corresponding author: Cheng-Yu Yeh  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Kuen-Jang Tsai, Wei-Cheng Yeh, Cheng-Yi Kao, Ming-Wei Lin, Chao-Ming Hung, Hung-Ying Chi, Cheng-Yu Yeh, and Shaw-Hwa Hwang, Mammographic Breast Composition Classification Using Swin Transformer Network, Sens. Mater., Vol. 36, No. 5, 2024, p. 1951-1957. |
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