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
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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. |
Forthcoming Regular Issues
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Applications of Novel Sensors and Related Technologies for Internet of Things
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Special Issue on International Conference on Biosensors, Bioelectronics, Biomedical Devices, BioMEMS/NEMS and Applications 2023 (Bio4Apps 2023)
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Special Issue on Sensing and Information and Communication Technologies toward Non-intrusive, Undisturbed, and Calm Healthcare Monitoring
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- A Prototype Portable Voltammetric Sensor for Determining Titratable Acidity of Sake and Moromi
Akira Kotani, Kokoro Taniguchi, Koichi Machida, Kazuhiro Yamamoto, and Hideki Hakamata - PACKTEST for L-Glutamate Quantification: Development of On-site and High-throughput Analytical Kits using L-Glutamate Oxidase Mutant
Keita Murai, Hiroki Yamaguchi, Satoru Furuuchi, Kazutoshi Takahashi, Uno Tagami, Moemi Tatsumi, Toshimi Mizukoshi, Hiroshi Miyano, Shuntaro Okauchi, and Masayuki Sugiki - Development of Portable Multi-fluorescence Detection System Using Indium Tin Oxide Heater for Loop-mediated Isothermal Amplification
Ryo Ishii, Sota Hirose, Shoji Yamamoto, Kazuhiro Morioka, Akihide Hemmi, and Hizuru Nakajima - Improved Substrate Specificity of Phenylalanine Dehydrogenase for L-Phenylalanine Sensor
Moemi Tatsumi, Kazutoshi Takahashi, Hiroki Yamaguchi, Uno Tagami, Hiroshi Miyano, Toshimi Mizukoshi, and Masayuki Sugiki - Reproduction of Absorption Spectra of Bromothymol Blue–Methyl Red Mixed Indicator from RGB and L*a*b* Color Coordinates and Application to Fast Spectrum Acquisition
Yusuke Kimura, Arinori Inagawa, and Nobuo Uehara - Orthogonality of α-Sulfoquinovosidase in Human Cells and Development of Its Fluorescent Substrate
Ryosuke Yoshida, Ryosei Kaguma, Ryosuke Kaneko, Ichiro Matuso, Makoto Yoritate, Go Hirai, Takamasa Teramoto, Yoshimitsu Kakuta, Kosuke Minamihata, Noriho Kamiya, Teruki Nii, Akihiro Kishimura, Takeshi Mori, and Yoshiki Katayama
- A Prototype Portable Voltammetric Sensor for Determining Titratable Acidity of Sake and Moromi
- Accepted papers (click here)
- Healthcare System from Multisensor Collaboration and Human Action Recognition
Hongwei Gao, Xuna Wang, Zide Liu, and Yueqiu Jiang
- Healthcare System from Multisensor Collaboration and Human Action Recognition
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- Mapping of Yangtze River Estuary Tidal Flat Based on Sentinel-2 Sensor and Google Earth Engine
Kuifeng Luan, Haixia Wan, Zhenhua Wang, Xuejun Lu, Zhuyuan Bei, Jiansheng Yuan, Zhaoxiang Cao, Xinyi You, Jie Wang, Wei Shen, and Junfang Bi
- Mapping of Yangtze River Estuary Tidal Flat Based on Sentinel-2 Sensor and Google Earth Engine
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Special Issue on Sensing and Data Analysis Technologies for Living Environment, Health Care, Production Management, and Engineering/Science Education Applications (2024)
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Special Issue on Advanced Sensors Materials and Processes
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Special Issue on Advanced Sensing Technologies for Green Energy
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Special Issue on Spatial Information and Digital Twins for Built Environment Development
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Special Issue on Intelligent Sensing and Analysis for Human–Machine Interaction in Healthcare, Biomedical Engineering, and Human-Centered Industrie
Guest editor, Dalin Zhou (University of Portsmouth), Jiahui Yu (Zhejiang University), Yuichiro Toda (Okayama University), and Zhaojie Ju (University of Portsmouth)
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Guest editor, Prof. He Huang (Beijing University of Civil Engineering and Architecture) and Prof. Junxing Yang (Beijing University of Civil Engineering and Architecture)
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Special Issue on Smart Sensing Approaches for Low Carbon, Energy-efficient Manufacturing Processes
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Special Issue on Smart Sensors for Chemical and Agriculture Applications
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Special Issue on Advanced Sensor Materials, Processes, and Applications
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