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Feb 28, 2019 (publication date) through Mar 13, 2025
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World Journal of Radiology
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1949-8470
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
| For: | Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, Cui XW, Dietrich CF. Artificial intelligence in breast ultrasound. World J Radiol 2019; 11(2): 19-26 [PMID: 30858931 DOI: 10.4329/wjr.v11.i2.19] |
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| URL: | https://www.wjgnet.com/1949-8470/full/v11/i2/19.htm |
| Number | Citing Articles |
| 1 |
Ruobing Huang, Mingrong Lin, Haoran Dou, Zehui Lin, Qilong Ying, Xiaohong Jia, Wenwen Xu, Zihan Mei, Xin Yang, Yijie Dong, Jianqiao Zhou, Dong Ni. Boundary-rendering network for breast lesion segmentation in ultrasound images. Medical Image Analysis 2022; 80: 102478 doi: 10.1016/j.media.2022.102478
|
| 2 |
Boyu Zhang, Aleksandar Vakanski, Min Xian. BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations. IEEE Access 2023; 11: 79480 doi: 10.1109/ACCESS.2023.3298569
|
| 3 |
Marco A. V. M. Grinet, Ana I. R. Gouveia, Abel J. P. Gomes. Machine learning in breast cancer imaging: a review on data, models and methods. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2024; 11(7) doi: 10.1080/21681163.2024.2302387
|
| 4 |
Ghufran B. Alghanimi, Hadeel K. Aljobouri, Khaleel Akeash Al-shimmari. CNN and ResNet50 Model Design for Improved Ultrasound Thyroid Nodules Detection. 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) 2024; : 1000 doi: 10.1109/ICETSIS61505.2024.10459588
|
| 5 |
Louis J. Catania. Foundations of Artificial Intelligence in Healthcare and Bioscience. 2021; : 125 doi: 10.1016/B978-0-12-824477-7.00005-5
|
| 6 |
Manuel Duarte Lobo. Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines. Advances in Medical Education, Research, and Ethics 2023; : 80 doi: 10.4018/978-1-6684-7164-7.ch004
|
| 7 |
Jinzhu Wei, Haoyang Zhang, Jiang Xie. A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception. Current Oncology 2024; 31(9): 5057 doi: 10.3390/curroncol31090374
|
| 8 |
Yongxin Guo, Yufeng Zhou. MS-CFNet: a multi-scale clinical studying-based and feature extraction-guided network for breast fibroadenoma segmentation in ultrasonography. Biomedical Engineering Letters 2024; 14(1): 173 doi: 10.1007/s13534-023-00330-7
|
| 9 |
Nesil Bor, Talya Tümer Sivri, Nergis Pervan Akman, Ali Berkol, Yahya Ekici. Breast Cancer Detection Using Various Classification Models Combined with Transfer Learning. 2022 International Conference on Artificial Intelligence of Things (ICAIoT) 2022; : 1 doi: 10.1109/ICAIoT57170.2022.10121840
|
| 10 |
Claudia Maria Vogel-Minea, Werner Bader, Jens-Uwe Blohmer, Volker Duda, Christian Eichler, Eva Maria Fallenberg, André Farrokh, Michael Golatta, Ines Gruber, Bernhard-Joachim Hackelöer, Jörg Heil, Helmut Madjar, Ellen Marzotko, Eberhard Merz, Markus Müller-Schimpfle, Alexander Mundinger, Ralf Ohlinger, Uwe Peisker, Fritz KW Schäfer, Ruediger Schulz-Wendtland, Christine Solbach, Mathias Warm, Dirk Watermann, Sebastian Wojcinski, Heiko Dudwiesus, Markus Hahn. Best Practice Guideline – Empfehlungen der DEGUM zur Durchführung und Beurteilung der Mammasonografie. Senologie - Zeitschrift für Mammadiagnostik und -therapie 2023; 20(04): 303 doi: 10.1055/a-2206-5288
|
| 11 |
Lu Liu, Kevin J. Parker, Sin-Ho Jung. Design and Analysis Methods for Trials with AI-Based Diagnostic Devices for Breast Cancer. Journal of Personalized Medicine 2021; 11(11): 1150 doi: 10.3390/jpm11111150
|
| 12 |
Iulia-Nela Anghelache Nastase, Simona Moldovanu, Luminita Moraru. Image Moment-Based Features for Mass Detection in Breast US Images via Machine Learning and Neural Network Classification Models. Inventions 2022; 7(2): 42 doi: 10.3390/inventions7020042
|
| 13 |
E. L. Teodozova, E. Yu. Khomutova. Artificial intelligence in radial diagnostics of breast cancer. Scientific Bulletin of the Omsk State Medical University 2023; 3(4): 26 doi: 10.61634/2782-3024-2023-12-26-35
|
| 14 |
Rui Du, Yanwei Chen, Tao Li, Liang Shi, Zhengdong Fei, Yuefeng Li, Xiaodong Li. Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network. Journal of Oncology 2022; 2022: 1 doi: 10.1155/2022/7733583
|
| 15 |
I. P. C. Buzatto, S. A. Recife, L. Miguel, R. M. Bonini, N. Onari, A. L. P. A. Faim, L. Silvestre, D. P. Carlotti, A. Fröhlich, D. G. Tiezzi. Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features. Breast Cancer Research and Treatment 2024; doi: 10.1007/s10549-024-07429-0
|
| 16 |
Tomoyuki Fujioka, Kazunori Kubota, Mio Mori, Yuka Kikuchi, Leona Katsuta, Mizuki Kimura, Emi Yamaga, Mio Adachi, Goshi Oda, Tsuyoshi Nakagawa, Yoshio Kitazume, Ukihide Tateishi. Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging. Diagnostics 2020; 10(7): 456 doi: 10.3390/diagnostics10070456
|
| 17 |
Seungjun Kim, Chanel Fischetti, Megan Guy, Edmund Hsu, John Fox, Sean D. Young. Artificial Intelligence (AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review. Diagnostics 2024; 14(15): 1669 doi: 10.3390/diagnostics14151669
|
| 18 |
Aashna Anand, Seungho Jung, Sukhan Lee. Breast Lesion Detection for Ultrasound Images Using MaskFormer. Sensors 2024; 24(21): 6890 doi: 10.3390/s24216890
|
| 19 |
Nitin Chaubal, Thomas Thomsen, Adnan Kabaalioglu, David Srivastava, Stephanie Simone Rösch, Christoph F. Dietrich. Ultrasound and contrast-enhanced ultrasound (CEUS) in infective liver lesions . Zeitschrift für Gastroenterologie 2021; 59(12): 1309 doi: 10.1055/a-1645-3138
|
| 20 |
Shuo Wang, Sihua Niu, Enze Qu, Flemming Forsberg, Annina Wilkes, Alexander Sevrukov, Kibo Nam, Robert F. Mattrey, Haydee Ojeda-Fournier, John R. Eisenbrey. Characterization of indeterminate breast lesions on B-mode ultrasound using automated machine learning models. Journal of Medical Imaging 2020; 7(05) doi: 10.1117/1.JMI.7.5.057002
|
| 21 |
Boran Zhou, Xiaofeng Yang, Walter J. Curran, Tian Liu. Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey. Journal of Ultrasound in Medicine 2022; 41(6): 1329 doi: 10.1002/jum.15819
|
| 22 |
Luca Nicosia, Francesca Addante, Anna Carla Bozzini, Antuono Latronico, Marta Montesano, Lorenza Meneghetti, Francesca Tettamanzi, Samuele Frassoni, Vincenzo Bagnardi, Rossella De Santis, Filippo Pesapane, Cristiana Iuliana Fodor, Mauro Giuseppe Mastropasqua, Enrico Cassano. Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists. Clinical Imaging 2022; 82: 150 doi: 10.1016/j.clinimag.2021.11.006
|
| 23 |
Heang-Ping Chan, Ravi K. Samala, Lubomir M. Hadjiiski. CAD and AI for breast cancer—recent development and challenges. The British Journal of Radiology 2019; 93(1108) doi: 10.1259/bjr.20190580
|
| 24 |
Sneha Singh, Nuala A. Healy. The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis. Insights into Imaging 2024; 15(1) doi: 10.1186/s13244-024-01869-4
|
| 25 |
Christopher Trepanier, Alice Huang, Michael Liu, Richard Ha. Emerging uses of artificial intelligence in breast and axillary ultrasound. Clinical Imaging 2023; 100: 64 doi: 10.1016/j.clinimag.2023.05.007
|
| 26 |
Yang Gu, Jia-Wei Tian, Hai-Tao Ran, Wei-Dong Ren, Cai Chang, Jian-Jun Yuan, Chun-Song Kang, You-Bin Deng, Hui Wang, Bao-Ming Luo, Sheng-Lan Guo, Qi Zhou, En-Sheng Xue, Wei-Wei Zhan, Qing Zhou, Jie Li, Ping Zhou, Chun-Quan Zhang, Man Chen, Ying Gu, Jin-Feng Xu, Wu Chen, Yu-Hong Zhang, Hong-Qiao Wang, Jian-Chu Li, Hong-Yan Wang, Yu-Xin Jiang. The Utility of the Fifth Edition of the BI-RADS Ultrasound Lexicon in Category 4 Breast Lesions: A Prospective Multicenter Study in China. Academic Radiology 2022; 29: S26 doi: 10.1016/j.acra.2020.06.027
|
| 27 |
Yu-Ting Hong, Zi-Han Yu, Chen-Pin Chou. Comparative Study of AI Modes in Ultrasound Diagnosis of Breast Lesions. Diagnostics 2025; 15(5): 560 doi: 10.3390/diagnostics15050560
|
| 28 |
Elham Amjad, Solmaz Asnaashari, Babak Sokouti, Siavoush Dastmalchi. Impact of Gene Biomarker Discovery Tools Based on Protein–Protein Interaction and Machine Learning on Performance of Artificial Intelligence Models in Predicting Clinical Stages of Breast Cancer. Interdisciplinary Sciences: Computational Life Sciences 2020; 12(4): 476 doi: 10.1007/s12539-020-00390-8
|
| 29 |
Ying Zhou, Bo-Jian Feng, Wen-Wen Yue, Yuan Liu, Zhi-Feng Xu, Wei Xing, Zhao Xu, Jin-Cao Yao, Shu-Rong Wang, Dong Xu. Differentiating non-lactating mastitis and malignant breast tumors by deep-learning based AI automatic classification system: A preliminary study. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.997306
|
| 30 |
Shuixin Yan, Jiadi Li, Weizhu Wu. Artificial intelligence in breast cancer: application and future perspectives. Journal of Cancer Research and Clinical Oncology 2023; 149(17): 16179 doi: 10.1007/s00432-023-05337-2
|
| 31 |
Maryann Hardy, Hugh Harvey. Artificial intelligence in diagnostic imaging: impact on the radiography profession. The British Journal of Radiology 2020; 93(1108) doi: 10.1259/bjr.20190840
|
| 32 |
Xin-Yi Wang, Li-Gang Cui, Jie Feng, Wen Chen. Artificial intelligence for breast ultrasound: An adjunct tool to reduce excessive lesion biopsy. European Journal of Radiology 2021; 138: 109624 doi: 10.1016/j.ejrad.2021.109624
|
| 33 |
Rudolf Hoffmann, Christoph Reich, Katrin Skerl. Evaluating different combination methods to analyse ultrasound and shear wave elastography images automatically through discriminative convolutional neural network in breast cancer imaging. International Journal of Computer Assisted Radiology and Surgery 2022; 17(12): 2231 doi: 10.1007/s11548-022-02737-6
|
| 34 |
Claudia Maria Vogel-Minea, Werner Bader, Jens-Uwe Blohmer, Volker Duda, Christian Eichler, Eva Maria Fallenberg, André Farrokh, Michael Golatta, Ines Gruber, Bernhard-Joachim Hackelöer, Jörg Heil, Helmut Madjar, Ellen Marzotko, Eberhard Merz, Markus Müller-Schimpfle, Alexander Mundinger, Ralf Ohlinger, Uwe Peisker, Fritz KW Schäfer, Ruediger Schulz-Wendtland, Christine Solbach, Mathias Warm, Dirk Watermann, Sebastian Wojcinski, Heiko Dudwiesus, Markus Hahn. Best Practice Guideline – Empfehlungen der DEGUM zur Durchführung und Beurteilung der Mammasonografie. Ultraschall in der Medizin - European Journal of Ultrasound 2023; 44(05): 520 doi: 10.1055/a-2020-9904
|
| 35 |
Marisa Wodrich, Jennie Karlsson, Kristina Lång, Ida Arvidsson. Medical Information Computing. Communications in Computer and Information Science 2025; 2240: 42 doi: 10.1007/978-3-031-79103-1_5
|
| 36 |
Jiliang Yang, Narasimhan Venkateswaran. The Influence of Intelligent Visual Sensing Technology on Online English Teaching in Wireless Network Environment. Wireless Communications and Mobile Computing 2022; 2022: 1 doi: 10.1155/2022/8282411
|
| 37 |
Bingxin Ma, Gang Wu, Haohui Zhu, Yifei Liu, Wenjia Hu, Jing Zhao, Yinlong Liu, Qiuyu Liu. The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer. Breast Cancer: Targets and Therapy 2025; : 145 doi: 10.2147/BCTT.S483496
|
| 38 |
Michal Byra, Piotr Jarosik, Katarzyna Dobruch-Sobczak, Ziemowit Klimonda, Hanna Piotrzkowska-Wroblewska, Jerzy Litniewski, Andrzej Nowicki. Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks. Ultrasonics 2022; 121: 106682 doi: 10.1016/j.ultras.2021.106682
|
| 39 |
Ryutaro Mori, Mai Okawa, Yoshihisa Tokumaru, Yoshimi Niwa, Nobuhisa Matsuhashi, Manabu Futamura. Application of an artificial intelligence-based system in the diagnosis of breast ultrasound images obtained using a smartphone. World Journal of Surgical Oncology 2024; 22(1) doi: 10.1186/s12957-023-03286-1
|
| 40 |
Imran Ul Haq, Haider Ali, Yuefeng Li, Zhe Liu. MAR-GAN: Multi attention residual generative adversarial network for tumor segmentation in breast ultrasounds. Biomedical Signal Processing and Control 2025; 100: 107171 doi: 10.1016/j.bspc.2024.107171
|
| 41 |
Xinxin Zhi, Junxiang Chen, Fangfang Xie, Jiayuan Sun, FelixJ. F. Herth. Diagnostic value of endobronchial ultrasound image features: A specialized review. Endoscopic Ultrasound 2021; 10(1): 3 doi: 10.4103/eus.eus_43_20
|
| 42 |
A Characterization Approach for the Review of CAD Systems Designed for Breast Tumor Classification Using B-Mode Ultrasound Images. Archives of Computational Methods in Engineering 2022; 29(3): 1485 doi: 10.1007/s11831-021-09620-8
|
| 43 |
Manisha Bahl. Updates in Artificial Intelligence for Breast Imaging. Seminars in Roentgenology 2022; 57(2): 160 doi: 10.1053/j.ro.2021.12.005
|
| 44 |
Ghufran Basim Alghanimi, Hadeel Aljobouri, Khaleel Akeash Alshimmari, Rasha Massoud. Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection. Al-Nahrain Journal for Engineering Sciences 2024; 27(4): 396 doi: 10.29194/NJES.27040396
|
| 45 |
Scott C. Hester, Maju Kuriakose, Christopher D. Nguyen, Srivalleesha Mallidi. Role of Ultrasound and Photoacoustic Imaging in Photodynamic Therapy for Cancer. Photochemistry and Photobiology 2020; 96(2): 260 doi: 10.1111/php.13217
|
| 46 |
Michal Byra, Katarzyna Dobruch-Sobczak, Ziemowit Klimonda, Hanna Piotrzkowska-Wroblewska, Jerzy Litniewski. Early Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Sonography Using Siamese Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics 2021; 25(3): 797 doi: 10.1109/JBHI.2020.3008040
|
| 47 |
Xiaoxi Huang, Youhui Qiu, Fangfang Bao, Juanhua Wang, Caifeng Lin, Yan Lin, Jianhang Wu, Haomin Yang. Artificial intelligence breast ultrasound and handheld ultrasound in the BI-RADS categorization of breast lesions: A pilot head to head comparison study in screening program. Frontiers in Public Health 2023; 10 doi: 10.3389/fpubh.2022.1098639
|
| 48 |
Yeşim Eroğlu, Muhammed Yildirim, Ahmet Çinar. Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Computers in Biology and Medicine 2021; 133: 104407 doi: 10.1016/j.compbiomed.2021.104407
|
| 49 |
Piotr Jarosik, Ziemowit Klimonda, Marcin Lewandowski, Michal Byra. Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks. Biocybernetics and Biomedical Engineering 2020; 40(3): 977 doi: 10.1016/j.bbe.2020.04.002
|
| 50 |
Fatih DEMİR. Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 2022; 34(2): 761 doi: 10.35234/fumbd.1142207
|
| 51 |
Dhurgham Al-Karawi, Shakir Al-Zaidi, Khaled Ahmad Helael, Naser Obeidat, Abdulmajeed Mounzer Mouhsen, Tarek Ajam, Bashar A. Alshalabi, Mohamed Salman, Mohammed H. Ahmed. A Review of Artificial Intelligence in Breast Imaging. Tomography 2024; 10(5): 705 doi: 10.3390/tomography10050055
|
| 52 |
Baiyan Qi, Xinyu Tian, Lei Fu, Yi Li, Kai San Chan, Chuxuan Ling, Wonjun Yim, Shiming Zhang, Jesse V. Jokerst, Xin Liu. Deep learning assisted sparse array ultrasound imaging. PLOS ONE 2023; 18(10): e0293468 doi: 10.1371/journal.pone.0293468
|
| 53 |
Manuel José Cruz Duarte Lobo, Sérgio Carlos Castanheira Nunes Miravent Tavares. Handbook of Research on Improving Allied Health Professions Education. Advances in Medical Education, Research, and Ethics 2022; : 186 doi: 10.4018/978-1-7998-9578-7.ch012
|
| 54 |
Orlando Catalano, Roberta Fusco, Federica De Muzio, Igino Simonetti, Pierpaolo Palumbo, Federico Bruno, Alessandra Borgheresi, Andrea Agostini, Michela Gabelloni, Carlo Varelli, Antonio Barile, Andrea Giovagnoni, Nicoletta Gandolfo, Vittorio Miele, Vincenza Granata. Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice. Diagnostics 2023; 13(5): 980 doi: 10.3390/diagnostics13050980
|
| 55 |
Karen Olivia Bazzo Goulart, Maximiliano Cassilha Kneubil, Janaina Brollo, Bruna Caroline Orlandin, Leandro Luis Corso, Mariana Roesch-Ely, João Antonio Pêgas Henriques. Use of artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer. Mastology 2023; 33 doi: 10.29289/2594539420220041
|
| 56 |
Hyun Jo Youn, Hyeong Eun Jeong, Ha Rim Ahn, Sang Yull Kang, Sung Hoo Jung. Diagnostic Utility of Artificial Intelligence in Breast Ultrasound. Journal of Surgical Ultrasound 2023; 10(1): 8 doi: 10.46268/jsu.2023.10.1.8
|
| 57 |
Jehad Cheyi, Yasemin Çetin Kaya. Advanced CNN-Based Classification and Segmentation for Enhanced Breast Cancer Ultrasound Imaging. Gazi University Journal of Science Part A: Engineering and Innovation 2024; 11(4): 647 doi: 10.54287/gujsa.1529857
|
| 58 |
Michal Byra, Piotr Jarosik, Aleksandra Szubert, Michael Galperin, Haydee Ojeda-Fournier, Linda Olson, Mary O’Boyle, Christopher Comstock, Michael Andre. Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomedical Signal Processing and Control 2020; 61: 102027 doi: 10.1016/j.bspc.2020.102027
|
| 59 |
Tara A. Retson, Mohammad Eghtedari. Computer-Aided Detection/Diagnosis in Breast Imaging: A Focus on the Evolving FDA Regulations for Using Software as a Medical Device. Current Radiology Reports 2020; 8(6) doi: 10.1007/s40134-020-00350-6
|
| 60 |
Monica Lupsor-Platon, Teodora Serban, Alexandra Iulia Silion, George Razvan Tirpe, Alexandru Tirpe, Mira Florea. Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease. Cancers 2021; 13(4): 790 doi: 10.3390/cancers13040790
|
| 61 |
Yu-Meng Lei, Miao Yin, Mei-Hui Yu, Jing Yu, Shu-E Zeng, Wen-Zhi Lv, Jun Li, Hua-Rong Ye, Xin-Wu Cui, Christoph F. Dietrich. Artificial Intelligence in Medical Imaging of the Breast. Frontiers in Oncology 2021; 11 doi: 10.3389/fonc.2021.600557
|
| 62 |
Rama Rao Malla, Vedavathi Katneni. Computational Methods in Drug Discovery and Repurposing for Cancer Therapy. 2023; : 73 doi: 10.1016/B978-0-443-15280-1.00004-2
|
| 63 |
Zong Fan, Ping Gong, Shanshan Tang, Christine U. Lee, Xiaohui Zhang, Pengfei Song, Shigao Chen, Hua Li. Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework. Medical Image Analysis 2023; 90: 102960 doi: 10.1016/j.media.2023.102960
|
| 64 |
Bo Lin, Zhibo Tan, Yaqi Mo, Xue Yang, Yajie Liu, Bo Xu. Intelligent oncology: The convergence of artificial intelligence and oncology. Journal of the National Cancer Center 2023; 3(1): 83 doi: 10.1016/j.jncc.2022.11.004
|
| 65 |
Yongxin Guo, Yufeng Zhou. Expansive Receptive Field and Local Feature Extraction Network: Advancing Multiscale Feature Fusion for Breast Fibroadenoma Segmentation in Sonography. Journal of Imaging Informatics in Medicine 2024; 37(6): 2810 doi: 10.1007/s10278-024-01142-6
|
| 66 |
A M Velasquez, T Velásquez-Pérez, A M Puentes. Optimization of the allocation of academic schedules through artificial intelligence techniques. Journal of Physics: Conference Series 2019; 1403(1): 012019 doi: 10.1088/1742-6596/1403/1/012019
|
| 67 |
Chunxiao Li, Yuanfan Guo, Liqiong Jia, Minghua Yao, Sihui Shao, Jing Chen, Yi Xu, Rong Wu. A Convolutional Neural Network Based on Ultrasound Images of Primary Breast Masses: Prediction of Lymph-Node Metastasis in Collaboration With Classification of Benign and Malignant Tumors. Frontiers in Physiology 2022; 13 doi: 10.3389/fphys.2022.882648
|
| 68 |
Yanan Liu, Xiaoyan Wang, Jingyu Li, Liguo Hao, Tianyu Zhao, He Zou, Dongbin Xu, Osamah Ibrahim Khalaf. Deep Learning Technology in Pathological Image Analysis of Breast Tissue. Journal of Healthcare Engineering 2021; 2021: 1 doi: 10.1155/2021/9610830
|
| 69 |
Michal Byra. Breast mass classification with transfer learning based on scaling of deep representations. Biomedical Signal Processing and Control 2021; 69: 102828 doi: 10.1016/j.bspc.2021.102828
|
| 70 |
Zhidong Xuan, Ting Ma, Yue Qin, Yajie Guo. Role of Ultrasound Imaging in the Prediction of TRIM67 in Brain Metastases From Breast Cancer. Frontiers in Neurology 2022; 13 doi: 10.3389/fneur.2022.889106
|
| 71 |
Tomoyuki Fujioka, Mio Mori, Kazunori Kubota, Jun Oyama, Emi Yamaga, Yuka Yashima, Leona Katsuta, Kyoko Nomura, Miyako Nara, Goshi Oda, Tsuyoshi Nakagawa, Yoshio Kitazume, Ukihide Tateishi. The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review. Diagnostics 2020; 10(12): 1055 doi: 10.3390/diagnostics10121055
|
| 72 |
Wanting Zhang, Huisi Wu, Jing Qin. Computer Vision – ECCV 2024. Lecture Notes in Computer Science 2025; 15081: 20 doi: 10.1007/978-3-031-73337-6_2
|
| 73 |
Fernando Pérez-Cota, Rafael Fuentes-Domínguez, Salvatore La Cavera, William Hardiman, Mengting Yao, Kerry Setchfield, Emilia Moradi, Shakila Naznin, Amanda Wright, Kevin F. Webb, Alan Huett, Claire Friel, Virginie Sottile, Hany M. Elsheikha, Richard J. Smith, Matt Clark. Picosecond ultrasonics for elasticity-based imaging and characterization of biological cells. Journal of Applied Physics 2020; 128(16) doi: 10.1063/5.0023744
|
| 74 |
Nicole Brunetti, Massimo Calabrese, Carlo Martinoli, Alberto Stefano Tagliafico. Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review. Diagnostics 2022; 13(1): 58 doi: 10.3390/diagnostics13010058
|
| 75 |
Qin Yang, Yu Tong. PalScDiff: A diffusion-based framework with progressive augmentation learning and semantic consistency for breast ultrasound tumor segmentation. Journal of Intelligent & Fuzzy Systems 2024; : 1 doi: 10.3233/JIFS-239703
|
| 76 |
Zi-Han Yu, Yu-Ting Hong, Chen-Pin Chou. Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors. Ultrasound in Medicine & Biology 2024; 50(9): 1372 doi: 10.1016/j.ultrasmedbio.2024.05.012
|
| 77 |
Avice M. O'Connell, Tommaso V. Bartolotta, Alessia Orlando, Sin‐Ho Jung, Jihye Baek, Kevin J. Parker. Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound. Journal of Ultrasound in Medicine 2022; 41(1): 97 doi: 10.1002/jum.15684
|
| 78 |
Haixia Liu, Guozhong Cui, Yi Luo, Yajie Guo, Lianli Zhao, Yueheng Wang, Abdulhamit Subasi, Sengul Dogan, Turker Tuncer. Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator. International Journal of General Medicine 2022; : 2271 doi: 10.2147/IJGM.S347491
|
| 79 |
Giovanni Irmici, Maurizio Cè, Gianmarco Della Pepa, Elisa D'Ascoli, Claudia De Berardinis, Emilia Giambersio, Lidia Rabiolo, Ludovica La Rocca, Serena Carriero, Catherine Depretto, Gianfranco Scaperrotta, Michaela Cellina.
Exploring the Potential of Artificial Intelligence in Breast Ultrasound
. Critical Reviews™ in Oncogenesis 2024; 29(2): 15 doi: 10.1615/CritRevOncog.2023048873
|
| 80 |
João M. Felício, Raquel A. Martins, Jorge R. Costa, Carlos A. Fernandes. Microwave Breast Imaging for Cancer Diagnosis: An overview [Bioelectromagnetics]. IEEE Antennas and Propagation Magazine 2024; 66(4): 85 doi: 10.1109/MAP.2024.3411480
|
| 81 |
Rezwan Ahmed Mahedi, Hrishik Iqbal, Raiyan Azmee, Marzan Azmee, Fatiha Jakir, Mufassir Ahmad Nishan, Mohammed Burhan Uddin, Sadia Afrin. Current Trends and Future Prospects of Artificial Intelligence in Transforming Radiology. Journal of Current Health Sciences 2024; 4(2): 95 doi: 10.47679/jchs.202487
|
| 82 |
Jaeil Kim, Hye Jung Kim, Chanho Kim, Jin Hwa Lee, Keum Won Kim, Young Mi Park, Hye Won Kim, So Yeon Ki, You Me Kim, Won Hwa Kim. Weakly-supervised deep learning for ultrasound diagnosis of breast cancer. Scientific Reports 2021; 11(1) doi: 10.1038/s41598-021-03806-7
|