A deep learning framework for supporting the classification of breast lesions in ultrasound images

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759

Contour Detection and Hierarchical Image Segmentation

Arbeláez, P., Maire, M., Fowlkes, C. C., & Malik, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,581

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

Navab, N., Joachim Hornegger, A., Wells, W. M., & Frangi, A. F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9 · Cited by 2,288

Computer-aided diagnosis in medical imaging: historical review, current status and future potential

Doi, K. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.02.002 · Cited by 1,855

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

Roche, A., Malandain, G., Pennec, X., & Ayache, N. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056181 · Cited by 1,578

Computer-aided diagnosis of breast cancer in ultrasonography images by deep learning

Qi, X., Yi, F., Zhang, L., Chen, Y., Pi, Y., Chen, Y., Guo, J., Wang, J., Guo, Q., Li, J., et al. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.047 · Cited by 43

Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China

Zhao, C., Xiao, M., Jiang, Y., Liu, H., Wang, M., Wang, H., Sun, Q., & Zhu, Q. (2019) · Cancer Management and Research · doi:10.2147/cmar.s190966 · Cited by 38

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Ali, S., Li, J., Pei, Y., Khurram, R., Rehman, K. U., & Rasool, A. B. (2021) · Cancers · doi:10.3390/cancers13215546 · Cited by 38

Machine learning in optical coherence tomography angiography

Le, D., Son, T., & Yao, X. (2021) · Experimental Biology and Medicine · doi:10.1177/15353702211026581 · Cited by 37

Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images

Zhu, C., Chai, X., Xiao, Y., Liu, X., Zhang, R., Yang, Z., & Wang, Z. (2024) · Diagnostics · doi:10.3390/diagnostics14030269 · Cited by 38

Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model

Baek, J., Basavarajappa, L., Hoyt, K., & Parker, K. J. (2021) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2021.3137644 · Cited by 35

Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions

Osapoetra, L. O., Chan, W., Tran, W. T., Kolios, M. C., & Czarnota, G. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0244965 · Cited by 39

Deep learning in breast imaging

Bhowmik, A., & Eskreis‐Winkler, S. (2022) · BJR|Open · doi:10.1259/bjro.20210060 · Cited by 37

Computer‐Aided Diagnosis of Solid Breast Lesions With Ultrasound: Factors Associated With False‐negative and False‐positive Results

Wu, J., Zhao, Z., Zhang, W., Liang, M., Ou, B., Yang, H.-Y., & Luo, B. (2019) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15020 · Cited by 34

IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound

Chanti, D. A., Duque, V. G., Crouzier, M., Nordez, A., Lacourpaille, L., & Mateus, D. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3058303 · Cited by 35

BUSnet: A Deep Learning Model of Breast Tumor Lesion Detection for Ultrasound Images

Li, Y., Gu, H., Wang, H., Qin, P., & Wang, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.848271 · Cited by 35

Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation

Wu, L., Bin, D., Liu, X., Hong, W., Chen, L., Gao, K., Sheng, Q., Yu, Y., Zhao, L., & Zhang, Y. (2022) · Frontiers in Pediatrics · doi:10.3389/fped.2021.770182 · Cited by 35

MF-Net: Multiple-feature extraction network for breast lesion segmentation in ultrasound images

Wang, J., Liu, G.-Q., Liu, D., & Chang, B. (2024) · Expert systems with applications · doi:10.1016/j.eswa.2024.123798 · Cited by 30

A domain knowledge-based interpretable deep learning system for improving clinical breast ultrasound diagnosis

Lin, Y., Liang, Z., Zhang, H., Zhang, G., Zheng, W., Han, C., Yu, D., Zhang, H., Xie, X., Liu, C., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00518-7 · Cited by 42

Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks

Jarosik, P., Klimonda, Z., Lewandowski, M., & Byra, M. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.04.002 · Cited by 34

Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net

Carton, F.-X., Chabanas, M., Lann, F. L., & Noble, J. H. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.3.031503 · Cited by 32

Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer

Benning, L., Peintner, A., & Peintner, L. (2022) · Cancers · doi:10.3390/cancers14030623 · Cited by 34

Joint Localization and Classification of Breast Cancer in B-Mode Ultrasound Imaging via Collaborative Learning With Elastography

Ding, W., Wang, J., Zhou, W., Zhou, S., Chang, C., & Shi, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3186933 · Cited by 32

CapsCovNet: A Modified Capsule Network to Diagnose COVID-19 From Multimodal Medical Imaging

Saif, A. F. M., Imtiaz, T., Rifat, S., Shahnaz, C., Zhu, W., & Ahmad, M. O. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3104791 · Cited by 32

Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks

Byra, M., Jarosik, P., Dobruch‐Sobczak, K., Klimonda, Z., Piotrzkowska‐Wróblewska, H., Litniewski, J., & Nowicki, A. (2022) · Ultrasonics · doi:10.1016/j.ultras.2021.106682 · Cited by 31

LVNet: Lightweight Model for Left Ventricle Segmentation for Short Axis Views in Echocardiographic Imaging

Awasthi, N., Vermeer, L., Fixsen, L. S., Lopata, R. G. P., & Pluim, J. P. W. (2022) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2022.3169684 · Cited by 32

Breast ultrasound lesion classification based on image decomposition and transfer learning

Zhuang, Z., Kang, Y., Raj, A. N. J., Yuan, Y., Ding, W., & Qiu, S. (2020) · Medical Physics · doi:10.1002/mp.14510 · Cited by 32

Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation

Evain, E., Raynaud, C., Ciofolo, C., Popoff, A., Caramella, T., Kbaier, P., Balleyguier, C., Harguem, S., Dapvril, H., Ceugnart, L., et al. (2021) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2021.09.002 · Cited by 30

Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study

Gu, Y., Xu, W., Liu, T., An, X., Tian, J., Ran, H., Ren, W., Chang, C., Yuan, J., Kang, C., et al. (2022) · European Radiology · doi:10.1007/s00330-022-09263-8 · Cited by 30

Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?

Lee, C., Chen, G.-L., Zhang, Z.-X., Chou, Y., & Hsu, C. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/8413403 · Cited by 28

Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning

Byra, M., Sznajder, T., Koržinek, D., Piotrzkowska‐Wróblewska, H., Dobruch‐Sobczak, K., Nowicki, A., & Marasek, K. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31332-6_4 · Cited by 27

A Novel Interpretable Computer-Aided Diagnosis System of Thyroid Nodules on Ultrasound Based on Clinical Experience

Zhang, S., Du, H., Jin, Z., Zhu, Y., Zhang, Y., Fang, X., Zhang, M., Tian, X., Zhang, J., & Luo, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2976495 · Cited by 26

Breast Ultrasound Image Classification Using a Pre-Trained Convolutional Neural Network

Daoud, M. I., Abdel-Rahman, S., & Alazrai, R. (2019) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2019.00037 · Cited by 27

Achieving highly efficient breast ultrasound tumor classification with deep convolutional neural networks

Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-00901-4 · Cited by 27

Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms

Murtaza, G., Shuib, L. M., Wahab, A. W. A., Mujtaba, G., & Raza, G. M. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08692-1 · Cited by 26

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

Ma, H., Tian, R., Li, H., Sun, H., Lu, G., Liu, R., & Wang, Z. (2021) · BioMedical Engineering OnLine · doi:10.1186/s12938-021-00950-z · Cited by 29

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Magnuska, Z., Theek, B., Darguzyte, M., Palmowski, M., Stickeler, E., Schulz, V., & Kiessling, F. M. (2022) · Cancers · doi:10.3390/cancers14020277 · Cited by 26

High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment

Czajkowska, J., Juszczyk, J., Piejko, L., & Glenc-Ambroży, M. (2022) · Sensors · doi:10.3390/s22041478 · Cited by 25

Deep Learning-assisted Diagnosis of Breast Lesions on US Images: A Multivendor, Multicenter Study

Xiang, H., Wang, X., Xu, M., Zhang, Y., Zeng, S., Li, C., Liu, L., Deng, T., Tang, G., Yan, C., et al. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.220185 · Cited by 26

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Gao, Y., Лю, Б., Zhu, Y., Chen, L., Tan, M., Xiao, X., Yu, G., & Guo, Y. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-12b · Cited by 24

Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation

Czajkowska, J., Badura, P., Korzekwa, S., Płatkowska-Szczerek, A., & Słowińska, M. (2021) · Sensors · doi:10.3390/s21175846 · Cited by 24

Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus

Boice, E. N., Torres, S. I. H., Knowlton, Z. J., Berard, D., Gonzalez, J. M., Avital, G., & Snider, E. J. (2022) · Journal of Imaging · doi:10.3390/jimaging8090249 · Cited by 23

Automated Skin Lesion Classification on Ultrasound Images

Marosán-Vilimszky, P., Szalai, K., Horváth, A., Csabai, D., Füzesi, K., Csány, G., & Gyöngy, M. (2021) · Diagnostics · doi:10.3390/diagnostics11071207 · Cited by 22

A Multiscale Nonlocal Feature Extraction Network for Breast Lesion Segmentation in Ultrasound Images

Liu, G., Wang, J., Liu, D., & Chang, B. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3265107 · Cited by 22

An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions

Xiao, M., Zhao, C., Zhu, Q., Zhang, J., Liu, H., Li, J., & Jiang, Y. (2019) · Journal of Thoracic Disease · doi:10.21037/jtd.2019.12.10 · Cited by 22

Automated diagnosis of multi-plane breast ultrasonography images using deep neural networks

Pi, Y., Chen, Y., Deng, D., Qi, X., Li, J., Lv, Q., & Zhang, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.123 · Cited by 23

Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging

Zhang, N., Li, X.-T., Ma, L., Fan, Z., & Sun, Y. (2021) · Clinical Imaging · doi:10.1016/j.clinimag.2021.03.024 · Cited by 21

Artificial intelligence in breast imaging: potentials and challenges

Li, J.-W., Sheng, D., Chen, J., You, C., Liu, S., Xu, H., & Chang, C. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acfade · Cited by 22

MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework

Zhong, S., Tu, C., Dong, X., Feng, Q., Chen, W., & Zhang, Y. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107346 · Cited by 19

Artificial Intelligence in Ultrasound Imaging: Current Research and Applications

Wang, B. S., Liu, M. J.-B., Zhu, M. Z., & Eisenbrey, P. J. (2019) · Advanced ultrasound in diagnosis and therapy · doi:10.37015/audt.2019.190811 · Cited by 19

A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors

Xie, J., Song, X., Zhang, W., Dong, Q., Wang, Y., Li, F., & Wan, C. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abc5c7 · Cited by 18

A bimodal BI-RADS-guided GoogLeNet-based CAD system for solid breast masses discrimination using transfer learning

Assari, Z., Mahloojifar, A., & Ahmadinejad, N. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105160 · Cited by 19

Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image

Di, X., Zhong, S., & Zhang, Y. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106612 · Cited by 20

Machine Learning Models to Improve the Differentiation Between Benign and Malignant Breast Lesions on Ultrasound: A Multicenter External Validation Study

Huo, L., Tan, Y., Wang, S., Geng, C., Li, Y., Ma, X., Wang, B., He, Y., Yao, C., & Ouyang, T. (2021) · Cancer Management and Research · doi:10.2147/cmar.s297794 · Cited by 18

The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

Alsharif, W. M. (2023) · Saudi Medical Journal · doi:10.15537/smj.2023.44.2.20220611 · Cited by 18

Benign and malignant classification of breast tumor ultrasound images using conventional radiomics and transfer learning features: A multicenter retrospective study

Tian, R., Lu, G., Tang, S., Sang, L., Ma, H., Qian, W., & Yang, W. (2024) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2024.104117 · Cited by 18

A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors

Huang, Z., Yang, K., Tian, H., Wu, H., Tang, S., Cui, C., Shi, S., Jiang, Y., Chen, J., Xu, J., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02404-z · Cited by 17

Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application

Hayashida, T., Odani, E., Kikuchi, M., Nagayama, A., Seki, T., Takahashi, M., Futatsugi, N., Matsumoto, A., Murata, T., Watanuki, R., et al. (2022) · Cancer Science · doi:10.1111/cas.15511 · Cited by 17

Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors

Minoda, Y., Ihara, E., Fujimori, N., Nagatomo, S., Esaki, M., Hata, Y., Bai, X., Tanaka, Y., Ogino, H., Chinen, T., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-20863-8 · Cited by 17

Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review

Mohamad, D. N. F. P., Mashohor, S., Mahmud, R., Hanafi, M. B., & Bahari, N. B. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10511-6 · Cited by 18

Use of artificial intelligence in breast surgery: a narrative review

Seth, I., Lim, B., Joseph, K., Gracias, D., Xie, Y., Ross, R. J., & Rozen, W. M. (2024) · Gland Surgery · doi:10.21037/gs-23-414 · Cited by 21

Faster Region Convolutional Neural Networks Applied to Ultrasonic Images for Breast Lesion Detection and Classification

Wei, K., Wang, B., & Saniie, J. (2020) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit48999.2020.9208264 · Cited by 17

Added value of deep learning-based computer-aided diagnosis and shear wave elastography to b-mode ultrasound for evaluation of breast masses detected by screening ultrasound

Kim, M. Y., Kim, S., Kim, Y. S., Kim, E. S., & Chang, J. M. (2021) · Medicine · doi:10.1097/md.0000000000026823 · Cited by 16

DG-CNN: Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

Xie, X., Niu, J., Liu, X., Li, Q., Wang, Y., Han, J., & Tang, S. (2022) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-0192-0 · Cited by 17

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

Baek, J., O’Connell, A. M., & Parker, K. J. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac9bcc · Cited by 17

Value of Original and Generated Ultrasound Data Towards Training Robust Classifiers for Breast Cancer Identification

Munteanu, B.-Ş., Murariu, A., Nichitean, M., Pitac, L.-G., & Dioşan, L. S. (2024) · Information Systems Frontiers · doi:10.1007/s10796-024-10499-6 · Cited by 16

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception

Wei, J., Zhang, H., & Xie, J. (2024) · Current Oncology · doi:10.3390/curroncol31090374 · Cited by 15

Explainable Deep Learning for Breast Cancer Classification and Localization

Giammarco, M. D., Vitulli, C., Cirnelli, S., Masone, B., Santone, A., Cesarelli, M., Martinelli, F., & Mercaldo, F. (2024) · ACM Transactions on Computing for Healthcare · doi:10.1145/3702237 · Cited by 15

A multi‐task learning based approach for efficient breast cancer detection and classification

Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2022) · Expert Systems · doi:10.1111/exsy.13047 · Cited by 15

Evaluating breast ultrasound S-detect image analysis for small focal breast lesions

Xing, B., Chen, X., Wang, Y., Li, S., Liang, Y.-K., & Wang, D. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1030624 · Cited by 15

Prediction of microvascular invasion in hepatocellular carcinoma based on preoperative Gd-EOB-DTPA-enhanced MRI: Comparison of predictive performance among 2D, 2D-expansion and 3D deep learning models

Wang, T., Li, Z., Yu, H., Duan, C., Feng, W., Chang, L., Yu, J., Liu, F., Gao, J., Zang, Y., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.987781 · Cited by 15

Deep‐learning‐based renal artery stenosis diagnosis via multimodal fusion

Wang, X., Cai, S., Wang, H., Li, J., & Yang, Y. (2024) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14298 · Cited by 15

A tree-based multiclassification of breast tumor histopathology images through deep learning

Murtaza, G., Wahab, A. W. A., Raza, G. M., & Shuib, L. M. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101870 · Cited by 15

A Characterization Approach for the Review of CAD Systems Designed for Breast Tumor Classification Using B-Mode Ultrasound Images

Kriti, N. A., Virmani, J., & Agarwal, R. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09620-8 · Cited by 15

EMT-NET: Efficient Multitask Network for Computer-Aided Diagnosis of Breast Cancer

Shi, J., Vakanski, A., Xian, M., Ding, J., & Ning, C. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761438 · Cited by 15

Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography

Fukuda, T., Tsunoda, H. S., Yagishita, K., Naganawa, S., Hayashi, K., & Kurihara, Y. (2023) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2022.12.003 · Cited by 14

An Automatic Breast Tumor Detection and Classification including Automatic Tumor Volume Estimation Using Deep Learning Technique

Labcharoenwongs, P., Vonganansup, S., Chunhapran, O., Noolek, D., & Yampaka, T. (2023) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2023.24.3.1081 · Cited by 19

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images

Wang, C., Zhao, Y., Wan, M., Huang, L., Liao, L., Guo, L., Zhang, J., & Zhang, C. (2023) · Medicine · doi:10.1097/md.0000000000035868 · Cited by 14

Classification of multi‐feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks

Xu, P., Zhao, J., Wan, M., Song, Q., Su, Q., & Wang, D. (2024) · Medical Physics · doi:10.1002/mp.16946 · Cited by 14

Computer-assisted diagnosis for axillary lymph node metastasis of early breast cancer based on transformer with dual-modal adaptive mid-term fusion using ultrasound elastography

Gong, C., Wu, Y., Zhang, G., Liu, X. E., Zhu, X., Cai, N., & Li, J. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102472 · Cited by 14

Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use

Ghosh, A. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.09.040 · Cited by 13

A new CNN architecture for efficient classification of ultrasound breast tumor images with activation map clustering based prediction validation

Sivanandan, R., & Jayakumari, J. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02357-3 · Cited by 14

Multi‐Features‐Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

Zhuang, Z., Yang, Z., Zhuang, S., Raj, A. N. J., Yuan, Y., & Nersisson, R. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/9980326 · Cited by 14

Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images

Rasaee, H., & Rivaz, H. J. (2021) · IEEE International Ultrasonics Symposium · doi:10.1109/ius52206.2021.9593490 · Cited by 14

Deep Transfer Learning for Pancreatic Cancer Detection

Baldota, S., Sharma, S., & Malathy, C. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9580000 · Cited by 14

Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

Kareem, F. Q., & Abdulazeez, A. M. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4621288 · Cited by 13

Referenceless distortion correction of gradient-echo echo-planar imaging under inhomogeneous magnetic fields based on a deep convolutional neural network

Liao, P., Zhang, J., Zeng, K., Yang, Y., Cai, S., Guo, G., & Cai, C. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.07.010 · Cited by 14

Artificial Intelligence Based Diagnosis for Cervical Lymph Node Malignancy Using the Point-Wise Gated Boltzmann Machine

Zhang, Q., Liu, Y., Han, H., Shi, J., & Wang, W. (2018) · IEEE Access · doi:10.1109/access.2018.2873043 · Cited by 13

Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

admin, A., & Abdulazeez, A. M. (2021) · Fusion Practice and Applications · doi:10.54216/fpa.030102 · Cited by 13

ESR Essentials: artificial intelligence in breast imaging—practice recommendations by the European Society of Breast Imaging

Schiaffino, S., Bernardi, D., Healy, N. A., Marino, M. A., Romeo, V., Sechopoulos, I., Mann, R. M., & Pinker, K. (2025) · European Radiology · doi:10.1007/s00330-025-11954-x · Cited by 13

Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint

Gao, Y., Zhu, Y., Liu, B., Hu, Y., Yu, G., & Guo, Y. (2021) · Diagnostics · doi:10.3390/diagnostics11071177 · Cited by 12

Clinical Application of Computer-Aided Diagnosis for Breast Ultrasonography: Factors That Lead to Discordant Results in Radial and Antiradial Planes

Zhu, Y., Zhan, W., Jia, X., Liu, J., & Zhou, J. (2022) · Cancer Management and Research · doi:10.2147/cmar.s348463 · Cited by 12

Deep Learning based Breast Image Classification Study for Cancer Detection

Sarada, C., Dattatreya, V., & Lakshmi, K. V. (2023) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs57338.2023.10100206 · Cited by 12

WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound Imaging

Cui, X. W., Goudie, A., Blaivas, M., Chai, Y. J., Chammas, M. C., Dong, Y. W., Stewart, J. E., Jiang, T.-A., Liang, P., Sehgal, C. M., et al. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.10.016 · Cited by 16

Computer-aided cholelithiasis diagnosis using explainable convolutional neural network

Kumar, D., Mehta, M. A., Kotecha, K. V., & Kulkarni, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-85798-2 · Cited by 13

Deep learning-based quality-controlled spleen assessment from ultrasound images

Yuan, Z., Anton, E. P., Jogeesvaran, H., Smith, N., Inusa, B. P. D., & King, A. P. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103724 · Cited by 12

Classification of breast lesions using quantitative ultrasound biomarkers

Nizam, N. I., Ara, S., & Hasan, M. K. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101786 · Cited by 12

Computer-Aided Detection/Diagnosis in Breast Imaging: A Focus on the Evolving FDA Regulations for Using Software as a Medical Device

Retson, T., & Eghtedari, M. (2020) · Current Radiology Reports · doi:10.1007/s40134-020-00350-6 · Cited by 12

Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients

Xiao, M., Zhao, C., Li, J., Zhang, J., Liu, H., Wang, M., Ouyang, Y., Zhang, Y., Jiang, Y., & Zhu, Q. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01070 · Cited by 12

Comparison of different CNNs for breast tumor classification from ultrasound imagesPreprint

Lazo, J. F., Moccia, S., Frontoni, E., & Momi, E. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14517 · Cited by 20

Diagnostic Performance of Deep Learning in Video-Based Ultrasonography for Breast Cancer: A Retrospective Multicentre Study

Chen, J., Huang, Z., Jiang, Y., Wu, H., Tian, H., Cui, C., Shi, S., Tang, S., Xu, J., Dong, X., et al. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.01.012 · Cited by 11

Diagnostic Accuracy and Interobserver Reliability of Rotator Cuff Tear Detection With Ultrasonography Are Improved With Attentional Deep Learning

Wu, K.-T., Chen, P., Chou, W., Chang, C.-D., & Lien, J.-J. J. (2024) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2024.12.024 · Cited by 11

Deep Learning-Based Drug Screening for COVID-19 and Case Studies

Saravanan, K. M., Zhang, H., Hossain, M. T., Reza, M. S., & Wei, Y. (2021) · Methods in pharmacology and toxicology · doi:10.1007/7653_2020_58 · Cited by 13

Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function

Pi, Y., Li, Q., Qi, X., Deng, D., & Zhang, Y. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03140-5 · Cited by 12

Application of artificial intelligence in predicting malignancy risk in breast masses on ultrasound

Wanderley, M. C., Soares, C. M. A., Morais, M. M. M., Cruz, R. M., Lima, I., Chojniak, R., & Bitencourt, A. G. V. (2023) · Radiologia Brasileira · doi:10.1590/0100-3984.2023.0034 · Cited by 11

A Practical Implementation of Deep Learning Method for Supporting the Classification of Breast Lesions in Ultrasound Images

Han, S., Lee, S., & Lee, J.-R. (2019) · International journal of advanced smart convergence · doi:10.7236/ijasc.2019.8.1.24 · Cited by 3

CAS: Breast cancer diagnosis framework based on lesion region recognition in ultrasound images

Yi, S., Chen, Z., Yi, L., & She, F. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101707 · Cited by 14

An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures

Cao, Z., Duan, L., Yang, G., Yue, T., & Chen, Q. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0349-x · Cited by 179

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

Baek, J., O’Connell, A. M., & Parker, K. J. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac9bcc · Cited by 17

Improving the diagnosis of breast cancer based on biophysical ultrasound features utilizing machine learningPreprint

Baek, J., O’Connell, A. M., & Parker, K. J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.06560 · Cited by 1