A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets

Cited by (389)

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4. A Review on Automatic Classification of Breast Cancer Using Supervised Learning Strategies
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5. Introduction
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   Gleb A. Kuzmin, Mishka Gidwani, Tianjun Ma, Tingliang Zhuang, Mohamed E. Abazeed · 2019 · Applied Radiation Oncology
9. An Ensemble Classification Method Based on Deep Neural Networks for Breast Cancer Diagnosis
   Yan Gao, Amin Rezaeipanah · 2023 · INTELIGENCIA ARTIFICIAL
10. Artificial Intelligence - A Primer for Diagnosis and Interpretation of Breast Cancer
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11. Nomogram Utilizing ABVS Radiomics and Clinical Factors for Predicting ≤ 3 Positive Axillary Lymph Nodes in HR+ /HER2- Breast Cancer with 1–2 Positive Sentinel Nodes
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13. Correction Learning for Medical Image Segmentation
    Guang Zhang, Shuhao Dong, Hui Fang Xu, Hongyu Zhang, Yongjian Wu, Yanwei Zhang, Xiaoming Xi, Yilong Yin · 2019 · IEEE Access
14. Rethink Transfer Learning in Medical Image Classification
    Le Peng, Hengyue Liang, Taihui Li, Ju Sun · 2021 · arXiv (Cornell University)
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    Natalie M. Baughan, Hui Li, Lan Li, Matthew D. Embury, Isaiah Yim, Gary J. Whitman, Randa A. El‐Zein, Isabelle Bedrosian, Maryellen Lissak Giger · 2023 · Journal of medical imaging
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    Yuhan Yang, Yuan Yuan Li · 2026 · World Journal of Radiology
18. Breast Cancer Detection Based on Decision Fusion of Machine Learning Algorithms
    Rohit Yadav, Richa Sharma · 2021 · Communications in computer and information science
19. Retracted: Efficient Breast Cancer Prediction using Hybrid Deep Learning in mammographic images
    G. Jayandhi, J. S. Leena Jasmine, R. Seetharaman, S. Mary Joans, R. Priscilla Joy · 2022 · International Conference on Electronics and Renewable Systems (ICEARS) · Retracted
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21. Differentiating Primary and Recurrent Lesions in Patients with a History of Breast Cancer: A Comprehensive Review
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    Tomoyuki Fujioka, Shohei Fujita, Daiju Ueda, Rintaro Ito, Mariko Kawamura, Yasutaka Fushimi, Takahiro Tsuboyama, Masahiro Yanagawa, Akira Yamada, Fuminari Tatsugami et al. · 2024 · Magnetic Resonance in Medical Sciences
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    Shuai Wang, Lian-Yu Zhang, Huiqian Du, Yan Chen, Yao Zheng, Wenbo Mei · 2022 · International Conference on Bioinformatics and Computational Biology (ICBCB)
24. Classification of Breast Cancer Diagnosis Systems Using Artificial Intelligence Techniques: Survey
    loujain abokaff · 2022 · SN Computer Science
25. Artificial intelligence: The next frontier of perfusion imaging?
    Cian M. Scannell, Amedeo Chiribiri, Tim Leiner · 2023 · Advances in magnetic resonance technology and applications
26. Breast mass classification in ultrasound based on Kendall's shape manifold
    Michał Byra, Michael P. André · 2019 · arXiv (Cornell University)
27. Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging
    Qiyuan Hu, Heather M. Whitney, Maryellen Lissak Giger · 2019 · arXiv (Cornell University)
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    Cefa Karabağ, Martin L. Jones, Constantino Carlos Reyes‐Aldasoro · 2021 · bioRxiv (Cold Spring Harbor Laboratory)
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    Liyan Zhang, Ruiyan Xu, Jingde Zhao · 2023 · Journal of X-Ray Science and Technology
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    Jing Wang, Liyuan Chen, Michael J. Dohopolski, David Jonathan Sher · 2024 · Elsevier eBooks
31. Future Trends in Artificial Intelligence for Medical Applications
    Tinni Chaudhuri, Joyeta Ghosh · 2025 · Advances in medical diagnosis, treatment, and care (AMDTC) book series
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    Kristine Eldevik Fasmer, Ankush Gulati, Sunniva Lindås, Camilla Krakstad, Ingfrid S. Haldorsen · 2025 · European Journal of Nuclear Medicine and Molecular Imaging
33. Uncertainty-Aware Cross-Modal Attention for Breast Cancer Classification in Ultrasound Imaging
    Aiman Farooq, Chandisha Das, Deepak Mishra · 2025 · Lecture notes in computer science
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    Rui Tao, Hongru Li, Jingyi Lu, Yaxin Wang, Jian Zhou, Xia Yu · 2026 · Digital Health
35. CAD of Breast Cancer: A Decade-Long Review of Techniques for Mammography Analysis.
    Alfonso Rojas‐Domínguez, Héctor José Puga Soberanes, Manuel Ornelas-Rodríguez, Itzel Guerrero-Gasca · 2020
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    Fatih Demir · 2022 · Fırat Üniversitesi Mühendislik Bilimleri Dergisi
37. Artificial intelligence diagnosis based on breast ultrasound imaging.
    Zihan Lan, Yulan Peng · 2022 · PubMed Central
38. Breast imaging and deep learning: past, present, and future
    Sarah Eskreis‐Winkler, Jonas Teuwen, S. Benson · 2022 · Advances in magnetic resonance technology and applications
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    Safwan Wshah, Beilei Xu, John A. Steinharter, Clifford A. Reilly, Katelin M. Morrissette · 2022 · Journal of medical imaging
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    Shuai Wang, Xiaojuan Xu, Lian-Yu Zhang, Huiqian Du, Yan Chen, Wenbo Mei · 2025 · Measurement Science and Technology
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    Jean-Baptiste Guillaumin, Aymeric Nadjem, Léa Vigouroux, Ana Sibleyras, Mickaël Tanter, Jean‐François Aubry, Béatrice Berthon · 2025 · Physics in Medicine and Biology
46. A scoping and bibliometric review of deep learning techniques in breast cancer imaging: mapping the landscape and future directions
    Sorayya Rezayi, Mehrbakhsh Nilashi, Erfan Esmaeeli, Nahid Ramezanghorbani, Goli Arji, Hossein Ahmadi, Leila Shahmoradi, Maryam Zahmatkeshan · 2025 · Neural Computing and Applications
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    Virginia Mato‐Abad, Alejandro Pazos, Cristian Robert Munteanu, José Liñares-Blanco, Sara Alvarez-Gonzalez, José M. Vázquez-Naya, Nieves Pedreira, Jorge Amigo, Carlos Fernandez-Lozano · 2021 · Elsevier eBooks
49. Classification of Malignant and Benign Tumors
    Juan Wang, Issam El Naqa, Yongyi Yang · 2022 · Springer eBooks
50. A Hybrid Deep Learning Methodology for Breast Cancer Diagnosis using Magnetic Resonance Images
    Seyyedreza Mirbagheri, Maryam Momeni · 2022 · Research Square
51. Computer aided diagnosis system for breast cancer using deep learning.
    Asma Baccouche · 2022
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    José G. Tamez‐Peña · 2023 · Research Square
53. Deep Learning for Predicting Breast Cancer: A Systematic Review of Progress and Future Directions
    Kanika Kansal, Sanjiv Sharma · 2023 · Research Square
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    Juan José Gómez Valverde · 2018
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    Michał Byra, Mei Wu, Zhang, Xiaodong, Hyungseok Jang, Yajun Ma, Eric Yao Chang, Sameer B. Shah, Jiang Du · 2019 · arXiv (Cornell University)
56. Digital Recognition of Breast Cancer Using TakhisisNet
    Loris Nanni, Alessandra Lumini, Gianluca Maguolo · 2020 · Advances in medical technologies and clinical practice book series
57. 13 Future Applications: Radiomics and Deep Learning on Breast MRI
    2021 · Thieme Medical Publishers eBooks
58. Attention-based Interactions Network for Breast Tumor Classification with Multi-modality Images
    Xiao Hui Yang, Xiaoming Xi, Chuanzhen Xu, Liangyun Sun, Lingzhao Meng, Xiushan Nie · 2022 · Conference on Human System Interactions
59. Digital Recognition of Breast Cancer Using TakhisisNet
    Loris Nanni, Alessandra Lumini, Gianluca Maguolo · 2022 · IGI Global eBooks
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    Ganlu Ouyang, Zhebin Chen, Meng Dou, Xu Luo, Han Wen, Xiangbing Deng, Wenjian Meng, Yongyang Yu, Bing Wu, Dan Jiang et al. · 2022 · Research Square
61. Digital Recognition of Breast Cancer Using TakhisisNet
    Loris Nanni, Alessandra Lumini, Gianluca Maguolo · 2022 · IGI Global eBooks
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    Zhao-Yi Lin, Kuang Chen, Weixiang Chen, Jinfeng Li, Chenggang Li, Guo-Quan Song, Yan-Zhe Liu, Jin Wang, Rong Liu, Minggen Hu · 2023 · Research Square
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    Hua Yang, Yinan Xu, Mohan Dong, Ying Zhang, Jie Gong, Dong Jun Huang, Lichun Wei, Shuiping Gou, Lina Zhao · 2023 · Research Square
64. Supplementary Data 1 from Efficacy and Quality-of-Life Following Involved Nodal Radiotherapy for Head and Neck Squamous Cell Carcinoma: The INRT-AIR Phase II Clinical Trial
    David Jonathan Sher, Dominic Himchan Moon, Dat T. Vo, Jing Wang, Liyuan Chen, Michael J. Dohopolski, Randall S. Hughes, Baran D. Sumer, Chul Woo Ahn, Vladimir Avkshtol · 2023
65. Supplementary Data 1 from Efficacy and Quality-of-Life Following Involved Nodal Radiotherapy for Head and Neck Squamous Cell Carcinoma: The INRT-AIR Phase II Clinical Trial
    David Jonathan Sher, Dominic Himchan Moon, Dat T. Vo, Jing Wang, Liyuan Chen, Michael J. Dohopolski, Randall S. Hughes, Baran D. Sumer, Chul Woo Ahn, Vladimir Avkshtol · 2023
66. Supplementary Data 1 from Efficacy and Quality-of-Life Following Involved Nodal Radiotherapy for Head and Neck Squamous Cell Carcinoma: The INRT-AIR Phase II Clinical Trial
    David Jonathan Sher, Dominic Himchan Moon, Dat T. Vo, Jing Wang, Liyuan Chen, Michael J. Dohopolski, Randall S. Hughes, Baran D. Sumer, Chul Woo Ahn, Vladimir Avkshtol · 2023
67. Supplementary Data 1 from Efficacy and Quality-of-Life Following Involved Nodal Radiotherapy for Head and Neck Squamous Cell Carcinoma: The INRT-AIR Phase II Clinical Trial
    David Jonathan Sher, Dominic Himchan Moon, Dat T. Vo, Jing Wang, Liyuan Chen, Michael J. Dohopolski, Randall S. Hughes, Baran D. Sumer, Chul Woo Ahn, Vladimir Avkshtol · 2023
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    A. Jayanthi K B, B. Rajasekaran C, C. Praveenkumar S, D. Dhanalakshmi R, E. SureshKumar Ramasamy · 2023 · IEEE Region 10 Conference (TENCON)
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    Loïc Duron, Laure S Fournier · 2023 · Springer eBooks
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    Rohan Senthilkumar · 2024 · F1000Research
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    Регентова Ольга Сергеевна, Р. А. Пархоменко, Nikolay I. Sergeev, K. V. Bozhenko, Полушкин Павел Владимирович, V. А. Solodkiy · 2024 · Digital Diagnostics
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    Nesma Abd El-Mawla, Mohamed Abdou Berbar, Nawal Ahmed El-Fishawy, Mohamed A. El-Rashidy · 2025 · Journal of Systems Science and Systems Engineering
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    Zodwa Dlamini, Rodney Hull, John Auma, Rui Manuel Reis · 2025 · Elsevier eBooks
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    Gao, Yang, Weizhen Wang, Yuan Yang, Xu, Ziting, Yue Lin, Ting Lang, Lei, Shangtong, Yisheng Xiao, Yang, Wei, Weijun Huang et al. · 2024 · Figshare
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    Gao, Yang, Weizhen Wang, Yuan Yang, Xu, Ziting, Yue Lin, Ting Lang, Lei, Shangtong, Yisheng Xiao, Yang, Wei, Weijun Huang et al. · 2024 · Figshare
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