Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

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A Noise-Aware Robustness Evaluation Framework for Breast Cancer Classification in Ultrasound Imaging

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Federated Learning for Breast Cancer Classification

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Deep Learning-Based Early Detection of Breast Cancer: Improving Accuracy and Efficiency in Diagnosis for Enhanced Patient Outcomes

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Breast Tumor Image Synthesis based on Diffusion Probabilistic Model

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Clinical Prior-Guided Tumor Generation for Breast Ultrasound with Cross Domain Adaptation

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ADAptation: Reconstruction-Based Unsupervised Active Learning for Breast Ultrasound Diagnosis

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MambaCA-Net: a hybrid dual-stream architecture integrating temporal and spatial features for breast ultrasound analysis

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LRA-UNet: Large receptive-field attention-enhanced U-Net for breast lesion segmentation in ultrasound images

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Parametric Quantitative Ultrasound-Based Deep Learning for Breast Cancer Diagnosis

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Deep Learning in Mammography Breast Cancer Detection

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Taxonomy on Breast Cancer Analysis Using Neural Networks

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Automatic Breast Tumor Classification in Ultrasound Images Using Morphological Features and New Texture Analysis Based on Image Visibility Graph and Gabor Filters

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Wijata, A. M., & Nalepa, J. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10221997 · Cited by 1

Multi-scale Dual-Attention-Based U-Net for Breast Cancer Segmentation in Ultrasound Images

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Breast Cancer Detection Using B-Mode and Ultrasound Strain Imaging

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Deep Learning Techniques for Breast Cancer Diagnose Via Image Segmentation

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An End-to-End Multi-stage Network for Ultrasound Video Object Segmentation

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Cancer Guard: Early Detection of Breast Cancer

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Breast Cancer Detection System Using AI/ML

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Reliable Breast Cancer Detection from Ultrasound Images using Image Segmentation

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Breast Cancer Determination Using B-Mode Ultrasound Imaging Using Deep Learning Technique

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A Comparative Study of Deep Learning in Breast Ultrasound Lesion Detection: From Two-Stage to One-Stage, from Anchor-Based to Anchor-Free

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Optimizing Breast Cancer Detection with Histopathological Images: A Machine Learning and Deep Learning Approach

Kalaiyarasi, M., Shanmugam, K., & Rajaguru, H. (2024) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca63461.2024.10801045 · Cited by 1

Breast Ultrasound Image Classification Using Transfer Learning-Based Feature Fusion Mode

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A scoping and bibliometric review of deep learning techniques in breast cancer imaging: mapping the landscape and future directions

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IHPE: A Lightweight Hand Pose Estimation Network for Surgical Motion Capture in Medical Education

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Combining CNNs and Transformers Networks for Improved Breast Ultrasound Image Segmentation

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Deep Learning-Based Study for Differentiating Normal Tissue and Masses in Korean Breast Ultrasound Images

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Data Acquisition and Processing of Breast Cancer Assisted Diagnosis Based on Ultrasound Imaging

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An Introduction to Deep Learning Techniques in Ultrasound Image Modality

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Breast lesion segmentation and characterization using the Small Tumor-Aware Network (STAN) and 2D/3D shape descriptors in ultrasound images

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A Quantitative Analysis for Breast Cancer Prediction Using Artificial Neural Network and Support Vector Machine

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Retinal Vessel Segmentation Using Multi-scale Generative Adversarial Network with Class Activation Mapping

Yang, M., Ye, Y., Ye, K., Hu, X., & Hu, B. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-06368-8_7 · Cited by 1

Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi

Demir, F. (2022) · Fırat Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.35234/fumbd.1142207 · Cited by 1

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Medical-Network (Med-Net): A Neural Network for Breast Cancer Segmentation in Ultrasound Image

Alzahrani, Y., & Boufama, B. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-7742-8_12 · Cited by 1

A Study of Breast Cancer Identification with Deep Learning Techniques

Priya, D. S., & Radha, V. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9304-6_67 · Cited by 1

Pre-training with Simulated Ultrasound Images for Breast Mass Segmentation and Classification

Byra, M., Klimonda, Z., & Litniewski, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44992-5_4 · Cited by 1

Breast Cancer Stage Identification Using Machine Learning

Arega, B. S., & Mengistu, T. M. (2024) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-64151-0_3 · Cited by 1

A Deep Representation Learning Framework for Medical Imaging Data Analysis

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Medical Datasets Collections for Artificial Intelligence-based Medical Image AnalysisPreprint

Wen, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01549 · Cited by 1

2018 Index IEEE Journal of Biomedical and Health Informatics Vol. 22

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Lavanya, G., & Thilagavathi, G. (2022) · Wiley eBooks · doi:10.1002/9781119819165.ch4 · Cited by 1

Gabor-based U-Net crevasse detection with ground penetrating radar data

Wang, M., Chen, D., Zhao, B., & Liu, Y. (2022) · International Conference on Ground Penetrating Radar (GPR) · doi:10.1190/gpr2022-091.1 · Cited by 1

Computer aided diagnosis system for breast cancer using deep learning.

Baccouche, A. (2022) · ThinkIR: The University of Louisville's Institutional Repository (University of Louisville) · doi:10.18297/etd/3931 · Cited by 1

Is despeckling necessary for deep learning based ultrasound image segmentation?Preprint

Liu, F., Chen, L., Qin, P., Xu, S., Dong, Z., Zhao, X., Wan, X., Chen, X., & Qin, B. (2023) · doi:10.36227/techrxiv.22186456.v1 · Cited by 1

Is denoising necessary for ultrasound image segmentation deep learning: review and benchmarkPreprint

Liu, F., Dong, Z., Qin, P., Qin, B., Xu, S., Zhao, X., Wan, X., Chen, X., & Chen, L. (2023) · doi:10.36227/techrxiv.22186456 · Cited by 1

Is despeckling necessary for deep learning based ultrasound image segmentation?Preprint

Liu, F., Chen, L., Qin, P., Xu, S., Dong, Z., Zhao, X., Wan, X., Chen, X., & Qin, B. (2023) · doi:10.36227/techrxiv.22186456.v2 · Cited by 1

Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and BeyondPreprint

Khan, N., Abraham, N., Hon, M., & Guan, L. (2023) · doi:10.32920/22734299 · Cited by 1

Development of a fast and accurate method for the segmentation of diabetic foot ulcer images

Mwawado, R. H. (2020) · doi:10.58694/20.500.12479/913 · Cited by 1

Segmentation and Analysis of Diagnostic Images for Lesion Detection: Current Status and Future Potential

Bhattacharjee, D. (2018) · Research in Medical & Engineering Sciences · doi:10.31031/rmes.2018.03.000573

A novel region growing approach using similarity set score and homogeneity based on neutrosophic set for ultrasound image segmentation

Jiang, X., Guo, Y., & Lu, Y. (2019) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2524144

Using Convolutional Neural Networks with Direct Acyclic Graph Architecture in Segmentation of Breast Lesions in US Images

Costa, M. G. F., Mendes, J. P. C., Pereira, W. C. D. A., & Filho, C. F. F. C. (2019) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-30648-9_99

Determination of Breast Cancer Using Neural Networks

Mahalakshmi, B., Vidhya, D., Niroja, N., & Mohankumar, P. (2019) · IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) · doi:10.1109/incos45849.2019.8951395

An End-to-End Solution for Automatic Contouring of Tumor Region in Intraoperative Images of Breast Lumpectomy

Gerolami, J., Wu, V., Fauerbach, P. V. N., Jabs, D., Engel, C. J., Rudan, J. F., Merchant, S. J., Walker, R., Anas, E. M. A., Abolmaesumi, P., et al. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176505

Ultrasound and artificial intelligence

Telfer, B. A., Kumar, V., Aguirre, A. D., Samir, A. E., & Brattain, L. J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00008-7

Improving the diagnostic performance of ultrasound in classifying breast lesions: the potential value of S-detect for residents-in-trainingPreprint

Zhao, C., Xiao, M., Jiang, Y., Liu, H., Wang, M., Wang, H., Zhang, J., & Zhu, Q. (2019) · doi:10.21203/rs.2.10429/v1

Recognition of Benign and Malignant Breast Ultrasound Images Based on Deep Transfer LearningPreprint

Wu, K., Xu, B., & Wu, Y. N. (2021) · Research Square · doi:10.21203/rs.3.rs-249760/v1

Estimation of the ambit of breast cancer with a modified ResNet analysis using machine learning approach

Narayanappa, C. K., Poornima, G. R., & Hiremath, B. V. (2021) · International Conference on Computing, Communication and Control System (I3CAC) · doi:10.4108/eai.7-6-2021.2308600

A Review of Transfer Learning for Medical Image Analysis

Hee, K. E., Alejandro, A., Maros, M. E., Santhanam, N., Jannesari, M., & Ganslandt, T. (2021)

Uterine Movement Analysis Based on Velocity Information by Convolutional Neural Networks

Mori, K., Tokunaga, Y., Sakumoto, T., Nakashima, A., Komesu, I., & Hata, Y. (2021) · World Automation Congress (WAC) · doi:10.23919/wac50355.2021.9559483

Towards Improved Medical Image Segmentation Using Deep Learning

Abraham, N. (2021) · doi:10.32920/ryerson.14663766.v1

Evaluating the Performance of Convolutional Neural Networks with Direct and Sequential Acyclic Graph Architectures in Automatic Segmentation of Breast Lesions in Ultrasound Images

Aquino, G., Serrão, M. K. M., Costa, M. G. F., & Costa-Filho, C. F. F. (2022) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-70601-2_237

Unsupervised Learning Method Via Triple Reconstruction for the Classification of Ultrasound Breast TumorsPreprint

Song, M., & Kim, Y. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3989658

Adaptive Information Assimilation using Convolutional Neural Network for Forecast of Breast Cancer from Electronic Health RecordsPreprint

Jagatheesaperumal, S. K., Kamala Devi, K., & Sekar, J. R. (2019) · doi:10.21203/rs.2.18006/v1

A Novel Framework for Ultrasound Image Enhancement to assist segmentation in Computer-Aided Detection of Breast LesionPreprint

Singh, M., Saini, I., & Sood, N. (2022) · Research Square · doi:10.21203/rs.3.rs-1552793/v1

A feasible method to determine the depth of myometrial invasion of endometrial carcinoma based on improved thinning algorithm

Gao, H., Chen, C., Mao, W., Weng, W., Li, Z., & Lin, Y. (2021) · International Conference on Information Technology in Medicine and Education · doi:10.1109/itme53901.2021.00085

Classification of Breast Tumor from Ultrasound Images Using No-Reference Image Quality Assessment

Dey, R., Bhattacharjee, D., Kollmann, C., & Krejcar, O. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0105-8_33

Breast Ultrasound Image Processing

Ramamoorthy, S. A., & Gopi, V. P. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-7136-4.ch031

Transformer-Based Radiomics for Predicting Breast Tumor Malignancy Score in Ultrasonography

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