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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]
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 imagesMedical 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 ExplanationsIEEE 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 methodsComputer 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 Detection2024 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 Bioscience2021; : 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 DisciplinesAdvances in Medical Education, Research, and Ethics 2023; : 80 doi: 10.4018/978-1-6684-7164-7.ch004
7
Yongxin Guo, Yufeng Zhou. MS-CFNet: a multi-scale clinical studying-based and feature extraction-guided network for breast fibroadenoma segmentation in ultrasonographyBiomedical Engineering Letters 2024; 14(1): 173 doi: 10.1007/s13534-023-00330-7
8
Nesil Bor, Talya Tümer Sivri, Nergis Pervan Akman, Ali Berkol, Yahya Ekici. Breast Cancer Detection Using Various Classification Models Combined with Transfer Learning2022 International Conference on Artificial Intelligence of Things (ICAIoT) 2022; : 1 doi: 10.1109/ICAIoT57170.2022.10121840
9
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 MammasonografieSenologie - Zeitschrift für Mammadiagnostik und -therapie 2023; 20(04): 303 doi: 10.1055/a-2206-5288
10
Lu Liu, Kevin J. Parker, Sin-Ho Jung. Design and Analysis Methods for Trials with AI-Based Diagnostic Devices for Breast CancerJournal of Personalized Medicine 2021; 11(11): 1150 doi: 10.3390/jpm11111150
11
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 ModelsInventions 2022; 7(2): 42 doi: 10.3390/inventions7020042
12
E. L. Teodozova, E. Yu. Khomutova. Artificial intelligence in radial diagnostics of breast cancerScientific Bulletin of the Omsk State Medical University 2023; 3(4): 26 doi: 10.61634/2782-3024-2023-12-26-35
13
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 NetworkJournal of Oncology 2022; 2022: 1 doi: 10.1155/2022/7733583
14
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 ImagingDiagnostics 2020; 10(7): 456 doi: 10.3390/diagnostics10070456
15
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
16
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 modelsJournal of Medical Imaging 2020; 7(05) doi: 10.1117/1.JMI.7.5.057002
17
Boran Zhou, Xiaofeng Yang, Walter J. Curran, Tian Liu. Artificial Intelligence in Quantitative Ultrasound Imaging: A SurveyJournal of Ultrasound in Medicine 2022; 41(6): 1329 doi: 10.1002/jum.15819
18
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 radiologistsClinical Imaging 2022; 82: 150 doi: 10.1016/j.clinimag.2021.11.006
19
Heang-Ping Chan, Ravi K. Samala, Lubomir M. Hadjiiski. CAD and AI for breast cancer—recent development and challengesThe British Journal of Radiology 2019; 93(1108) doi: 10.1259/bjr.20190580
20
Christopher Trepanier, Alice Huang, Michael Liu, Richard Ha. Emerging uses of artificial intelligence in breast and axillary ultrasoundClinical Imaging 2023; 100: 64 doi: 10.1016/j.clinimag.2023.05.007
21
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 ChinaAcademic Radiology 2022; 29: S26 doi: 10.1016/j.acra.2020.06.027
22
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 CancerInterdisciplinary Sciences: Computational Life Sciences 2020; 12(4): 476 doi: 10.1007/s12539-020-00390-8
23
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 studyFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.997306
24
Shuixin Yan, Jiadi Li, Weizhu Wu. Artificial intelligence in breast cancer: application and future perspectivesJournal of Cancer Research and Clinical Oncology 2023; 149(17): 16179 doi: 10.1007/s00432-023-05337-2
25
Maryann Hardy, Hugh Harvey. Artificial intelligence in diagnostic imaging: impact on the radiography professionThe British Journal of Radiology 2020; 93(1108) doi: 10.1259/bjr.20190840
26
Xin-Yi Wang, Li-Gang Cui, Jie Feng, Wen Chen. Artificial intelligence for breast ultrasound: An adjunct tool to reduce excessive lesion biopsyEuropean Journal of Radiology 2021; 138: 109624 doi: 10.1016/j.ejrad.2021.109624
27
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 imagingInternational Journal of Computer Assisted Radiology and Surgery 2022; 17(12): 2231 doi: 10.1007/s11548-022-02737-6
28
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 MammasonografieUltraschall in der Medizin - European Journal of Ultrasound 2023; 44(05): 520 doi: 10.1055/a-2020-9904
29
Jiliang Yang, Narasimhan Venkateswaran. The Influence of Intelligent Visual Sensing Technology on Online English Teaching in Wireless Network EnvironmentWireless Communications and Mobile Computing 2022; 2022: 1 doi: 10.1155/2022/8282411
30
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 networksUltrasonics 2022; 121: 106682 doi: 10.1016/j.ultras.2021.106682
31
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 smartphoneWorld Journal of Surgical Oncology 2024; 22(1) doi: 10.1186/s12957-023-03286-1
32
Xinxin Zhi, Junxiang Chen, Fangfang Xie, Jiayuan Sun, FelixJ. F. Herth. Diagnostic value of endobronchial ultrasound image features: A specialized reviewEndoscopic Ultrasound 2021; 10(1): 3 doi: 10.4103/eus.eus_43_20
33
A Characterization Approach for the Review of CAD Systems Designed for Breast Tumor Classification Using B-Mode Ultrasound ImagesArchives of Computational Methods in Engineering 2022; 29(3): 1485 doi: 10.1007/s11831-021-09620-8
34
Manisha Bahl. Updates in Artificial Intelligence for Breast ImagingSeminars in Roentgenology 2022; 57(2): 160 doi: 10.1053/j.ro.2021.12.005
35
Scott C. Hester, Maju Kuriakose, Christopher D. Nguyen, Srivalleesha Mallidi. Role of Ultrasound and Photoacoustic Imaging in Photodynamic Therapy for CancerPhotochemistry and Photobiology 2020; 96(2): 260 doi: 10.1111/php.13217
36
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 NetworksIEEE Journal of Biomedical and Health Informatics 2021; 25(3): 797 doi: 10.1109/JBHI.2020.3008040
37
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 programFrontiers in Public Health 2023; 10 doi: 10.3389/fpubh.2022.1098639
38
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 mRMRComputers in Biology and Medicine 2021; 133: 104407 doi: 10.1016/j.compbiomed.2021.104407
39
Piotr Jarosik, Ziemowit Klimonda, Marcin Lewandowski, Michal Byra. Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networksBiocybernetics and Biomedical Engineering 2020; 40(3): 977 doi: 10.1016/j.bbe.2020.04.002
40
Fatih DEMİR. Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit EdilmesiFırat Üniversitesi Mühendislik Bilimleri Dergisi 2022; 34(2): 761 doi: 10.35234/fumbd.1142207
41
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 ImagingTomography 2024; 10(5): 705 doi: 10.3390/tomography10050055
42
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 imagingPLOS ONE 2023; 18(10): e0293468 doi: 10.1371/journal.pone.0293468
43
Manuel José Cruz Duarte Lobo, Sérgio Carlos Castanheira Nunes Miravent Tavares. Handbook of Research on Improving Allied Health Professions EducationAdvances in Medical Education, Research, and Ethics 2022; : 186 doi: 10.4018/978-1-7998-9578-7.ch012
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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 PracticeDiagnostics 2023; 13(5): 980 doi: 10.3390/diagnostics13050980
45
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 cancerMastology 2023; 33 doi: 10.29289/2594539420220041
46
Hyun Jo Youn, Hyeong Eun Jeong, Ha Rim Ahn, Sang Yull Kang, Sung Hoo Jung. Diagnostic Utility of Artificial Intelligence in Breast UltrasoundJournal of Surgical Ultrasound 2023; 10(1): 8 doi: 10.46268/jsu.2023.10.1.8
47
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 networkBiomedical Signal Processing and Control 2020; 61: 102027 doi: 10.1016/j.bspc.2020.102027
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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 DeviceCurrent Radiology Reports 2020; 8(6) doi: 10.1007/s40134-020-00350-6
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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 DiseaseCancers 2021; 13(4): 790 doi: 10.3390/cancers13040790
50
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 BreastFrontiers in Oncology 2021; 11 doi: 10.3389/fonc.2021.600557
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Rama Rao Malla, Vedavathi Katneni. Computational Methods in Drug Discovery and Repurposing for Cancer Therapy2023; : 73 doi: 10.1016/B978-0-443-15280-1.00004-2
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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 frameworkMedical Image Analysis 2023; 90: 102960 doi: 10.1016/j.media.2023.102960
53
Bo Lin, Zhibo Tan, Yaqi Mo, Xue Yang, Yajie Liu, Bo Xu. Intelligent oncology: The convergence of artificial intelligence and oncologyJournal of the National Cancer Center 2023; 3(1): 83 doi: 10.1016/j.jncc.2022.11.004
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A M Velasquez, T Velásquez-Pérez, A M Puentes. Optimization of the allocation of academic schedules through artificial intelligence techniquesJournal of Physics: Conference Series 2019; 1403(1): 012019 doi: 10.1088/1742-6596/1403/1/012019
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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 TumorsFrontiers in Physiology 2022; 13 doi: 10.3389/fphys.2022.882648
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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 TissueJournal of Healthcare Engineering 2021; 2021: 1 doi: 10.1155/2021/9610830
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Michal Byra. Breast mass classification with transfer learning based on scaling of deep representationsBiomedical Signal Processing and Control 2021; 69: 102828 doi: 10.1016/j.bspc.2021.102828
58
Zhidong Xuan, Ting Ma, Yue Qin, Yajie Guo. Role of Ultrasound Imaging in the Prediction of TRIM67 in Brain Metastases From Breast CancerFrontiers in Neurology 2022; 13 doi: 10.3389/fneur.2022.889106
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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 ReviewDiagnostics 2020; 10(12): 1055 doi: 10.3390/diagnostics10121055
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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 cellsJournal of Applied Physics 2020; 128(16) doi: 10.1063/5.0023744
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Nicole Brunetti, Massimo Calabrese, Carlo Martinoli, Alberto Stefano Tagliafico. Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid ReviewDiagnostics 2022; 13(1): 58 doi: 10.3390/diagnostics13010058
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Qin Yang, Yu Tong. PalScDiff: A diffusion-based framework with progressive augmentation learning and semantic consistency for breast ultrasound tumor segmentationJournal of Intelligent & Fuzzy Systems 2024; : 1 doi: 10.3233/JIFS-239703
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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 UltrasoundJournal of Ultrasound in Medicine 2022; 41(1): 97 doi: 10.1002/jum.15684
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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 GeneratorInternational Journal of General Medicine 2022; : 2271 doi: 10.2147/IJGM.S347491
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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
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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 cancerScientific Reports 2021; 11(1) doi: 10.1038/s41598-021-03806-7