Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

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

A Threshold Selection Method from Gray-Level Histograms

Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584

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

Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images

Costa, M. G. F., Campos, J. P. M., Aquino, G., Pereira, W. C. D. A., & Filho, C. F. F. C. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0389-2 · Cited by 25

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

Breast lesion detection using an anchor-free network from ultrasound images with segmentation-based enhancement

Wang, Y., & Yao, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-18747-y · Cited by 25

DSC-Net: A Novel Interactive Two-Stream Network by Combining Transformer and CNN for Ultrasound Image Segmentation

Hu, K., Zhu, Y., Zhou, T., Zhang, Y., Cao, C., Xiao, F., & Gao, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3322993 · Cited by 26

An Efficient Breast Cancer Segmentation System based on Deep Learning Techniques

Shaaban, S. M., Nawaz, M. A., Said, Y., & Barr, M. (2023) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.6518 · Cited by 25

Tabu Search and Machine‐Learning Classification of Benign and Malignant Proliferative Breast Lesions

Dhahri, H., Rahmany, I., Mahmood, A., Maghayreh, E. A., & Elkilani, W. S. (2020) · BioMed Research International · doi:10.1155/2020/4671349 · Cited by 25

UltraSegNet: A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

Abuowaida, S. F. A., Owida, H. A., Alsekait, D. M., Alshdaifat, N., AbdElminaam, D. S., & Alshinwan, M. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.063470 · Cited by 24

A Second-Order Subregion Pooling Network for Breast Lesion Segmentation in Ultrasound

Zhu, L., Chen, R., Fu, H., Xie, C., Wang, L., Wan, L., & Heng, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59725-2_16 · Cited by 24

Ultrasound deep learning for monitoring of flow–vessel dynamics in murine carotid artery

Park, J. H., Seo, E., Choi, W., & Lee, S. J. (2021) · Ultrasonics · doi:10.1016/j.ultras.2021.106636 · Cited by 24

DGANet: A Dual Global Attention Neural Network for Breast Lesion Detection in Ultrasound Images

Meng, H., Liu, X., Niu, J., Wang, Y., Liao, J., Li, Q., & Chen, C. (2022) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2022.07.006 · Cited by 24

Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors

Popperli, M., Gulagundi, R., Yogamani, S. K., & Milz, S. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8813879 · Cited by 24

Collaborative framework for automatic classification of respiratory sounds

Ntalampiras, S. (2020) · IET Signal Processing · doi:10.1049/iet-spr.2019.0487 · Cited by 24

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

FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise

Wu, N., Sun, Z., Yan, Z., & Yu, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i14.29525 · Cited by 23

Integrating metaheuristics and artificial intelligence for healthcare: basics, challenging and future directions

Houssein, E. H., Saber, E., Ali, A. A., & Wazery, Y. M. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10822-2 · Cited by 23

MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation

Xu, Q., Chen, Y., Li, Y., Liu, Z., Lou, Z., Zhang, Y., Zheng, H., & He, X. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3757324 · Cited by 23

Biomedical imaging and analysis through deep learning

Drukker, K., Yan, P., Sibley, A., & Wang, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00004-1 · Cited by 23

Hybrid UNET Model Segmentation for an Early Breast Cancer Detection Using Ultrasound Images

Ahmed, I. B., Ouarda, W., & Amar, C. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16014-1_37 · Cited by 23

Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis

Syed, A. H., & Khan, T. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.854927 · Cited by 24

Toward Intraoperative Margin Assessment Using a Deep Learning-Based Approach for Automatic Tumor Segmentation in Breast Lumpectomy Ultrasound Images

Veluponnar, D., de Boer, L. L., Geldof, F., Jong, L.-J. S., Guimaraes, M. D. S., Peeters, M.-J. T. F. D. V., van Duijnhoven, F. H., Ruers, T. J. M., & Dashtbozorg, B. (2023) · Cancers · doi:10.3390/cancers15061652 · Cited by 23

Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network

Shareef, B., Xian, M., Vakanski, A., & Wang, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43901-8_33 · Cited by 23

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

Cancer Detection Using Artificial Intelligence: A Paradigm in Early Diagnosis

Gayathri, B., Vidyasagar, K. E. C., Mudigonda, M., & Saikia, M. J. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10209-0 · Cited by 22

APG-SAM: Automatic prompt generation for SAM-based breast lesion segmentation with boundary-aware optimization

Yin, D., Zheng, Q., Chen, L., Hu, Y., & Wang, Q. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127048 · Cited by 22

3D tumor detection in automated breast ultrasound using deep convolutional neural network

Li, Y., Wu, W., Chen, H., Cheng, L., & Wang, S. (2020) · Medical Physics · doi:10.1002/mp.14477 · 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

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

U-Net with optimal thresholding for small blob detection in medical images

Xu, Y., Gao, F., Wu, T., Bennett, K. M., Charlton, J. R., & Sarkar, S. (2019) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8843234 · Cited by 22

Simultaneous segmentation and classification of breast lesions from ultrasound images using Mask R-CNN

Liang, Y., He, R., Li, Y., & Wang, Z. (2019) · IEEE International Ultrasonics Symposium · doi:10.1109/ultsym.2019.8926185 · Cited by 22

Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms

Carrilero-Mardones, M., Jurado, M. P., Nogales, A., Pérez‐Martín, J., & Díez, F. J. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01155-1 · Cited by 25

A new automated segmentation and classification of mammogram images

Patil, R. S., Biradar, N., & Pawar, R. V. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-11932-1 · Cited by 21

A dual-stage transformer and MLP-based network for breast ultrasound image segmentation

Lin, G., Chen, M., Tan, M., Chen, L., & Chen, J. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.09.001 · Cited by 21

An Adaptive Region Growing Based on Neutrosophic Set in Ultrasound Domain for Image Segmentation

Jiang, X., Guo, Y., Chen, H., Zhang, Y., & Lu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2911560 · Cited by 22

Adjusted Quick Shift Phase Preserving Dynamic Range Compression method for breast lesions segmentation

Osman, F. M., & Yap, M. H. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100344 · Cited by 23

A Novel Breast Tumor Classification in Ultrasound Images, Using Deep Convolutional Neural Network

Zeimarani, B., Costa, M. G. F., Nurani, N. Z., & Filho, C. F. F. C. (2019) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-13-2517-5_14 · Cited by 20

Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image

Lee, H., Lee, M. H., Youn, S., Lee, K., Lew, H. M., & Hwang, J. Y. (2022) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2022.3193640 · Cited by 20

Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning

Chen, Y., Zhang, C., Ding, C. H. Q., & Liu, L. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3228254 · Cited by 20

Semi-universal geo-crack detection by machine learning

Shi, Y., Ballesio, M., Johansen, K., Trentman, D., Huang, Y., McCabe, M. F., Bruhn, R. L., & Schuster, G. T. (2023) · Frontiers in Earth Science · doi:10.3389/feart.2023.1073211 · Cited by 20

Boundary-Oriented Network for Automatic Breast Tumor Segmentation in Ultrasound Images

Zhang, M., Huang, A., Yang, D., Xu, R., & Wu, Y. (2022) · Ultrasonic Imaging · doi:10.1177/01617346231162925 · Cited by 20

Autonomous Trajectory Planning for Ultrasound-Guided Real-Time Tracking of Suspicious Breast Tumor Targets

Tan, J., Li, J., Li, Y., Li, B., Leng, Y., Rong, Y. K., & Fu, C. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3262844 · Cited by 20

Effective hybrid attention network based on pseudo-color enhancement in ultrasound image segmentation

Huang, X., Wang, Q., Chen, J., Chen, L., & Chen, Z. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104742 · Cited by 20

Label-Decoupled Medical Image Segmentation With Spatial-Channel Graph Convolution and Dual Attention Enhancement

Jiang, Q., Ye, H., Yang, B., & Cao, F. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3367756 · Cited by 20

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging

Hoang, Q. T., Pham, X. H., Trinh, X. T., Le, A. V., Minh, B. V., & Bui, T. T. (2024) · Journal of Imaging · doi:10.3390/jimaging10040077 · Cited by 20

HTBE-Net: A hybrid transformer network based on boundary enhancement for breast ultrasound image segmentation

Feng, J., Dong, X., Liu, X., & Zheng, X. (2024) · Displays · doi:10.1016/j.displa.2024.102753 · Cited by 20

Vision Mamba for Classification of Breast Ultrasound Images

Nasiri-Sarvi, A., Hosseini, M. S., & Rivaz, H. J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77789-9_15 · Cited by 22

Deep Learning-Based Automatic Endometrium Segmentation and Thickness Measurement for 2D Transvaginal Ultrasound

Hu, S.-Y., Xu, H. M., Li, Q., Telfer, B. A., Brattain, L. J., & Samir, A. E. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856367 · Cited by 20

Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

Ramadan, H., Lachqar, C., & Tairi, H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101945 · Cited by 20

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

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

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

Jiménez-Gaona, Y., Rodríguez-Álvarez, M. J., & Lakshminarayanan, V. (2020) · Applied Sciences · doi:10.3390/app10228298 · Cited by 21

Shape-Adaptive Convolutional Operator for Breast Ultrasound Image Segmentation

Huang, K., Zhang, Y., Cheng, H.-D., & Xing, P. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428287 · Cited by 19

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

EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images

You, G., Yang, X., Lee, X. C., & Zhu, K. (2023) · IET Image Processing · doi:10.1049/ipr2.12966 · Cited by 19

EU2-Net: A Parameter Efficient Ensemble Model With Attention-Aided Triple Feature Fusion for Tumor Segmentation in Breast Ultrasound Images

Roy, A., Pramanik, P., & Sarkar, R. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3421436 · Cited by 19

An Interpretable Breast Ultrasound Image Classification Algorithm Based on Convolutional Neural Network and Transformer

Meng, X., Ma, J., Liu, F., Chen, Z., & Zhang, T. (2024) · Mathematics · doi:10.3390/math12152354 · Cited by 19

Shape-intensity-guided U-net for medical image segmentation

Dong, W., Du, B., & Xu, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128534 · Cited by 19

RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

Wang, H., Zhang, G., Zhao, Y., Lai, F., Cui, W., Xue, J., Wang, Q., Zhang, H., & Lin, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821972 · Cited by 18

D-DDPM: Deep Denoising Diffusion Probabilistic Models for Lesion Segmentation and Data Generation in Ultrasound Imaging

Alblwi, A., Makkawy, S. J., & Barner, K. E. (2025) · IEEE Access · doi:10.1109/access.2025.3548128 · Cited by 18

A computer-assisted medical diagnosis system for cancer diseases based on quaternion orthogonal Rademacher-Fourier moments and deep learning

Ogri, O. E., El-Mekkaoui, J., & Hjouji, A. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108744 · Cited by 18

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

Ursuleanu, T. F., Luca, A. R., Gheorghe, L., Grigorovici, R., Iancu, S., Hlusneac, M., Preda, C., & Grigorovici, A. (2021) · Diagnostics · doi:10.3390/diagnostics11081373 · Cited by 20

BraNet: a mobil application for breast image classification based on deep learning algorithms

Jiménez-Gaona, Y., Rodríguez-Álvarez, M. J., Castillo, D., García-Jaen, S., Carrión-Figueroa, D., Domínguez, P. E. C., & Lakshminarayanan, V. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03084-1 · Cited by 18

Applications of GAN Models in Breast Cancer Detection: A Comprehensive Review

Majidpour, J., Ahmed, H. A., Ahmed, M. H., Jalal, S. I., & Arabi, H. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10323-7 · Cited by 18

GPU acceleration of liver enhancement for tumor segmentation

Satpute, N., Naseem, R., Pelanis, E., Gómez-Luna, J., Cheikh, F. A., Elle, O. J., & Olivares, J. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105285 · Cited by 18

A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories

Chang, Y.-W., Chen, Y.-R., Ko, C.-C., Lin, W.-Y., & Lin, K. (2020) · Applied Sciences · doi:10.3390/app10051830 · Cited by 18

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

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

MR-Forest: A Deep Decision Framework for False Positive Reduction in Pulmonary Nodule Detection

Zhu, H., Zhao, H. Y., Song, C., Bian, Z., Bi, Y., Liu, T., He, X., Yang, D., & Cai, W. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2947506 · Cited by 18

A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation

Wang, T., Liu, J., & Tang, J. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01392-y · Cited by 17

MammXAI: An XAI integrated adaptive multi-model deep learning approach for breast cancer detection using multi-modality images

Singh, S. K., & Patnaik, K. S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109173 · Cited by 17

Investigating Shift Variance of Convolutional Neural Networks in Ultrasound Image Segmentation

Sharifzadeh, M., Benali, H., & Rivaz, H. J. (2022) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2022.3162800 · Cited by 17

Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization

Khan, M. B., Rahman, A., Nawaz, M. S., Ahmed, R., Khan, M. A., & Mosavi, A. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022373 · 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

MV-STCNet: Breast cancer diagnosis using spatial and temporal dual-attention guided classification network based on multi-view ultrasound videos

Kong, D., Hu, S., & Zhao, G. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105541 · Cited by 17

Multi-Instance Classification of Breast Tumor Ultrasound Images Using Convolutional Neural Networks and Transfer Learning

Ciobotaru, A., Bota, M. A., Gota, D.-I., & Miclea, L. C. (2023) · Bioengineering · doi:10.3390/bioengineering10121419 · Cited by 17

BPSegSys: A Brachial Plexus Nerve Trunk Segmentation System Using Deep Learning

Wang, Y., Zhu, B., Kong, L., Wang, J., Gao, B., Wang, J., Tian, D., & Yao, Y. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2023.11.009 · Cited by 17

A robust encoder decoder based weighted segmentation and dual staged feature fusion based meta classification for breast cancer utilizing ultrasound imaging

Himel, M. H. A. M. H., Chowdhury, P., & Hasan, M. A. M. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200367 · Cited by 19

Breast Cancer Segmentation From Ultrasound Images Using Multiscale Cascaded Convolution With Residual Attention-Based Double Decoder Network

Umer, M. J., Sharif, M. I., & Kim, J. (2024) · IEEE Access · doi:10.1109/access.2024.3429386 · Cited by 17

Automatic Segmentation of MRI Human Thigh Muscles

Shaubari, E. F. A., McPhee, J. S., Degens, H., & Yap, M. H. (2018) · International Conference on Biomedical Engineering and Technology (ICBET) · doi:10.1145/3208955.3208958 · Cited by 17

Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network

Golshan, M., Karimi, D., Mahdavi, S. S., Lobo, J., Peacock, M., Salcudean, S. E., & Spadinger, I. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab64b5 · Cited by 17

Multi-scale Gradational-Order Fusion Framework for Breast Lesions Classification Using Ultrasound Images

Ning, Z., Tu, C., Xiao, Q., Luo, J., & Zhang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59725-2_17 · Cited by 17

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

Automated seeding for ultrasound skin lesion segmentation

Marosán-Vilimszky, P., Szalai, K., Csabai, D., Csány, G., Horváth, A., & Gyöngy, M. (2020) · Ultrasonics · doi:10.1016/j.ultras.2020.106268 · Cited by 17

Ultrasound Anomaly Detection Based on Variational Autoencoders

Milković, F., Filipović, B., Subašić, M., Petković, T., Lončarić, S., & Budimir, M. (2021) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa52656.2021.9552041 · Cited by 17

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

Focal U-Net: A Focal Self-attention based U-Net for Breast Lesion Segmentation in Ultrasound Images

Zhao, H., Niu, J., Meng, H., Wang, Y., Li, Q., & Ziniu, Y. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9870824 · Cited by 17

Breast mass segmentation based on ultrasonic entropy maps and attention gated U-NetPreprint

Byra, M., Jarosik, P., Dobruch‐Sobczak, K., Klimonda, Z., Piotrzkowska‐Wróblewska, H., Litniewski, J., & Nowicki, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10061 · Cited by 17

Breast lesions segmentation from ultrasound images using DeepLabV3 + model with channel and spatial attention mechanism

Aggarwal, S., Garg, M., Kumar, A., & Kapila, R. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00424-x · Cited by 16

A novel method for image segmentation: two-stage decoding network with boundary attention

Cao, F., Gao, C., & Ye, H. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01459-6 · Cited by 16

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