1
|
Wang R, Liu X, Tan G. Coupling speckle noise suppression with image classification for deep-learning-aided ultrasound diagnosis. Phys Med Biol 2024; 69:065001. [PMID: 38359452 DOI: 10.1088/1361-6560/ad29bb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/15/2024] [Indexed: 02/17/2024]
Abstract
Objective. During deep-learning-aided (DL-aided) ultrasound (US) diagnosis, US image classification is a foundational task. Due to the existence of serious speckle noise in US images, the performance of DL models may be degraded. Pre-denoising US images before their use in DL models is usually a logical choice. However, our investigation suggests that pre-speckle-denoising is not consistently advantageous. Furthermore, due to the decoupling of speckle denoising from the subsequent DL classification, investing intensive time in parameter tuning is inevitable to attain the optimal denoising parameters for various datasets and DL models. Pre-denoising will also add extra complexity to the classification task and make it no longer end-to-end.Approach. In this work, we propose a multi-scale high-frequency-based feature augmentation (MSHFFA) module that couples feature augmentation and speckle noise suppression with specific DL models, preserving an end-to-end fashion. In MSHFFA, the input US image is first decomposed to multi-scale low-frequency and high-frequency components (LFC and HFC) with discrete wavelet transform. Then, multi-scale augmentation maps are obtained by computing the correlation between LFC and HFC. Last, the original DL model features are augmented with multi-scale augmentation maps.Main results. On two public US datasets, all six renowned DL models exhibited enhanced F1-scores compared with their original versions (by 1.31%-8.17% on the POCUS dataset and 0.46%-3.89% on the BLU dataset) after using the MSHFFA module, with only approximately 1% increase in model parameter count.Significance. The proposed MSHFFA has broad applicability and commendable efficiency and thus can be used to enhance the performance of DL-aided US diagnosis. The codes are available athttps://github.com/ResonWang/MSHFFA.
Collapse
Affiliation(s)
- Ruixin Wang
- College of Computer Science and Software Engineering, Hohai University, Nanjing 210098, People's Republic of China
| | - Xiaohui Liu
- The First People's Hospital of Kunshan, Affiliated Kunshan Hospital of Jiangsu University, Kunshan 215300, People's Republic of China
| | - Guoping Tan
- College of Computer Science and Software Engineering, Hohai University, Nanjing 210098, People's Republic of China
| |
Collapse
|
2
|
Hossain S, Azam S, Montaha S, Karim A, Chowa SS, Mondol C, Zahid Hasan M, Jonkman M. Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model. Heliyon 2023; 9:e21369. [PMID: 37885728 PMCID: PMC10598544 DOI: 10.1016/j.heliyon.2023.e21369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/11/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
Introduction Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates. Purpose The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study. Method Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images. Result The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset. Conclusion The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.
Collapse
Affiliation(s)
- Shahed Hossain
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Sami Azam
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
| | - Sidratul Montaha
- Department of Computer Science, University of Calgary, Calgary, AB, T2N 1N4, Canada
| | - Asif Karim
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
| | - Sadia Sultana Chowa
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Chaity Mondol
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Md Zahid Hasan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Mirjam Jonkman
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
| |
Collapse
|
3
|
Alhussan AA, Eid MM, Towfek SK, Khafaga DS. Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm. Biomimetics (Basel) 2023; 8:163. [PMID: 37092415 PMCID: PMC10123690 DOI: 10.3390/biomimetics8020163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 04/25/2023] Open
Abstract
According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women's death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments.
Collapse
Affiliation(s)
- Amel Ali Alhussan
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Marwa M. Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
| | - S. K. Towfek
- Delta Higher Institute for Engineering and Technology, Mansoura 35111, Egypt
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| |
Collapse
|
4
|
Jabeen K, Khan MA, Alhaisoni M, Tariq U, Zhang YD, Hamza A, Mickus A, Damaševičius R. Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion. SENSORS 2022; 22:s22030807. [PMID: 35161552 PMCID: PMC8840464 DOI: 10.3390/s22030807] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/12/2022] [Accepted: 01/17/2022] [Indexed: 12/11/2022]
Abstract
After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.
Collapse
Affiliation(s)
- Kiran Jabeen
- Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan; (K.J.); (M.A.K.); (A.H.)
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan; (K.J.); (M.A.K.); (A.H.)
| | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia;
| | - Usman Tariq
- College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj 11942, Saudi Arabia;
| | - Yu-Dong Zhang
- Department of Informatics, University of Leicester, Leicester LE1 7RH, UK;
| | - Ameer Hamza
- Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan; (K.J.); (M.A.K.); (A.H.)
| | - Artūras Mickus
- Department of Applied Informatics, Vytautas Magnus University, LT-44404 Kaunas, Lithuania;
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, LT-44404 Kaunas, Lithuania;
- Correspondence:
| |
Collapse
|
5
|
Huang K, Xu M, Qi X. NGMMs: Neutrosophic Gaussian Mixture Models for Breast Ultrasound Image Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3943-3947. [PMID: 34892094 DOI: 10.1109/embc46164.2021.9630448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ultrasound imaging is commonly used for diagnosing breast cancers since it is non-invasive and inexpensive. Breast ultrasound (BUS) image classification is still a challenging task due to the poor image quality and lack of public datasets. In this paper, we propose novel Neutrosophic Gaussian Mixture Models (NGMMs) to more accurately classify BUS images. Specifically, we first employ a Deep Neural Network (DNN) to extract features from BUS images and apply principal component analysis to condense extracted features. We then adopt neutrosophic logic to compute three probability functions to estimate the truth, indeterminacy, and falsity of an image and design a new likelihood function by using the neutrosophic logic components. Finally, we propose an improved Expectation Maximization (EM) algorithm to incorporate neutrosophic logic to reduce the weights of images with high indeterminacy and falsity when estimating parameters of each NGMM to better fit these images to Gaussian distributions. We compare the performance of the proposed NGMMs, its two peer GMMs, and three DNN-based methods in terms of six metrics on a new dataset combining two public datasets. Our experimental results show that NGMMs achieve the highest classification results for all metrics.
Collapse
|
6
|
Shen Y, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J, Wu N, Huddleston C, Wolfson S, Millet A, Ehrenpreis R, Awal D, Tyma C, Samreen N, Gao Y, Chhor C, Gandhi S, Lee C, Kumari-Subaiya S, Leonard C, Mohammed R, Moczulski C, Altabet J, Babb J, Lewin A, Reig B, Moy L, Heacock L, Geras KJ. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun 2021; 12:5645. [PMID: 34561440 PMCID: PMC8463596 DOI: 10.1038/s41467-021-26023-2] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/14/2021] [Indexed: 02/08/2023] Open
Abstract
Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.
Collapse
Affiliation(s)
- Yiqiu Shen
- grid.137628.90000 0004 1936 8753Center for Data Science, New York University, New York, NY USA
| | - Farah E. Shamout
- grid.440573.1Engineering Division, NYU Abu Dhabi, Abu Dhabi, UAE
| | - Jamie R. Oliver
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Jan Witowski
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Kawshik Kannan
- grid.482020.c0000 0001 1089 179XDepartment of Computer Science, Courant Institute, New York University, New York, NY USA
| | - Jungkyu Park
- grid.137628.90000 0004 1936 8753Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY USA
| | - Nan Wu
- grid.137628.90000 0004 1936 8753Center for Data Science, New York University, New York, NY USA
| | - Connor Huddleston
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Stacey Wolfson
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Alexandra Millet
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Robin Ehrenpreis
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Divya Awal
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Cathy Tyma
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Naziya Samreen
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Yiming Gao
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Chloe Chhor
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Stacey Gandhi
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Cindy Lee
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Sheila Kumari-Subaiya
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Cindy Leonard
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Reyhan Mohammed
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Christopher Moczulski
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Jaime Altabet
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - James Babb
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Alana Lewin
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Beatriu Reig
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Linda Moy
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA ,grid.137628.90000 0004 1936 8753Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY USA
| | - Laura Heacock
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Krzysztof J. Geras
- grid.137628.90000 0004 1936 8753Center for Data Science, New York University, New York, NY USA ,grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA ,grid.137628.90000 0004 1936 8753Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY USA
| |
Collapse
|
7
|
Ding Y, Sun C, Zhou Q, Cheng C, Yan C, Wang B. Use of Palpation Imaging in Diagnosis of Breast Diseases: A Way to Improve the Detection Rate. Med Sci Monit 2020; 26:e927553. [PMID: 33247894 PMCID: PMC7709466 DOI: 10.12659/msm.927553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Background Breast diseases pose increasing threat to women health as peoples lifestyle changes. The aim of this study was to investigate the clinical application value of Palpation Imaging (PI) in the diagnosis of breast diseases. Material/Methods From October 2019 to February 2020, 184 patients with 225 breast lesions were examined by using PI, ultrasound, and mammography in the department of Breast Surgery, the First Affiliated Hospital of Anhui Medical University. All cases were confirmed pathologically by core-needle biopsy or excisional biopsy. The cut-off value of the PI tests was determined by receiver operating characteristic (ROC) curve. We compared the examination results of PI with ultrasound and mammography to analyze the diagnostic value of PI. Results Pathological examination revealed that 186/225(82.67%) lesions were benign, while 39 were malignant. All 8 parameters of PI were significantly correlated with pathological findings (P<0.05). The best cut-off value for the PI score was 19.5 and the area under the curve (AUC) for the PI was 0.921 (95% CI: 0.874–0.968, P<0.001) with 89.7% sensitivity and 86.0% specificity. PI showed greater sensitivity (89.7%) and its specificity (86.0% vs. 86.4%, P=0.931) and accuracy (86.7% vs. 84.6%, P=0.604) were similar to those of mammography. The combination of 3 types of test is superior to a single examination. The sensitivity was 100% and the specificity was 98.8%. Conclusions PI has high clinical value in differentiation of benign and malignant breast lesions. Combination examination has the potential to improve the detection of breast cancer in screening and diagnostic capacities and can be used as a supplement to ultrasound and mammography.
Collapse
Affiliation(s)
- Yihan Ding
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China (mainland).,Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China (mainland)
| | - Chenyu Sun
- AMITA Health Saint Joseph Hospital Chicago, Chicago, IL, USA
| | - Qin Zhou
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Ce Cheng
- Department of Internal Medicine, The University of Arizona College of Medicine at South Campus, Tucson, AZ, USA
| | - Cunye Yan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing, China (mainland)
| | - Benzhong Wang
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China (mainland).,Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China (mainland)
| |
Collapse
|
8
|
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; 11:19-26. [PMID: 30858931 PMCID: PMC6403465 DOI: 10.4329/wjr.v11.i2.19] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 01/14/2019] [Accepted: 01/27/2019] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.
Collapse
Affiliation(s)
- Ge-Ge Wu
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Li-Qiang Zhou
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jian-Wei Xu
- Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Jia-Yu Wang
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Qi Wei
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - You-Bin Deng
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xin-Wu Cui
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Christoph F Dietrich
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Würzburg, Würzburg 97980, Germany
| |
Collapse
|