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A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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Combination of Aggregated Channel Features (ACF) Detector and Faster R-CNN to Improve Object Detection Performance in Fetal Ultrasound Images

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Deep Learning based Fetal Middle Cerebral Artery Segmentation in Large-scale Ultrasound Images

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

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Breast Lesion Segmentation in Ultrasound Images with Limited Annotated Data

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Early detection of breast cancer using mammography images and software engineering process

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Ultrasound tissue classification: a review

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Cross-Tissue/Organ Transfer Learning for the Segmentation of Ultrasound Images Using Deep Residual U-Net

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Fusion Siamese network with drift correction for target tracking in ultrasound sequences

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

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Breast cancer classification and segmentation framework using multiscale CNN and U‐shaped dual decoded attention network

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EU-Net: Enhanced U-shaped Network for Breast Mass Segmentation

Chowdary, G. J., & Yogarajah, P. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3266740 · Cited by 15

An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images

Azam, S., Montaha, S., Raiaan, M. A. K., Rafid, A. K. M. R. H., Mukta, S. H., & Jonkman, M. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00925-7 · Cited by 15

Design of hybrid neural networks of the ensemble structure

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Ultrasonic imaging using conditional generative adversarial networks

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A Multi-attention Triple Decoder Deep Convolution Network for Breast Cancer Segmentation Using Ultrasound Images

Umer, M. J., Sharif, M., & Raza, M. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10214-8 · Cited by 14

An efficient framework for lesion segmentation in ultrasound images using global adversarial learning and region-invariant loss

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Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach

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GBE-Net: Global Boundary Enhancement Network for breast lesion segmentation in ultrasound images

Feng, J., Dong, X., Chen, S., Zhou, L., & Zheng, X. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106644 · Cited by 14

New one-step model of breast tumor locating based on deep learning

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Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation

Kazemimoghadam, M., Chi, W., Rahimi, A., Kim, N., Alluri, P., Nwachukwu, C., Lu, W., & Gu, X. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac176d · Cited by 14

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

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Pixel-wise triplet learning for enhancing boundary discrimination in medical image segmentation

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Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance ImagesPreprint

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Generative Adversarial U-Net for Domain-free Medical Image AugmentationPreprint

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Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review

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Rethinking Multi-Center Semi-Supervised Breast Cancer Ultrasound Image Segmentation: An Intermediate-Domain Perspective

Ye, Z., Zhang, Y., Huang, J., Wang, D., Liu, S., Mei, L., & Lei, C. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3668217 · Cited by 13

A Spatial-Temporal Deformable Attention Based Framework for Breast Lesion Detection in Videos

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Multi-scale multi-object semi-supervised consistency learning for ultrasound image segmentation

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Shape-intensity knowledge distillation for robust medical image segmentation

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Automatic joint segmentation and classification of breast ultrasound images via multi-task learning with object contextual attention

Lu, Y., Sun, F., Wang, J., & Yu, K. M. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1567577 · Cited by 13

Deep learning-based quantitative analysis of dental caries using optical coherence tomography: an ex vivo study

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Optimal cropping for input images used in a convolutional neural network for ultrasonic diagnosis of liver tumors

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Breast Lesion Segmentation in Ultrasound Images Using Deep Convolutional Neural Networks

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Deep learning applied to breast imaging classification and segmentation with human expert intervention

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Improving the classification performance of breast ultrasound image using deep learning and optimization algorithm

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Mass detection in automated 3-D breast ultrasound using a patch Bi-ConvLSTM network

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HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining

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Survival Analysis for Multimode Ablation Using Self-Adapted Deep Learning Network Based on Multisource Features

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On-device Training for Breast Ultrasound Image Classification

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Challenge-aware U-net for breast lesion segmentation in ultrasound images

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A Detailed Investigation on Reduction of False Positive Rate in Breast Cancer Detection

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A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques

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Edge-guided adaptive multi-feature learning for breast ultrasound image segmentation

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Ultrasound breast images denoising using generative adversarial networks (GANs)

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AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions

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Joint Federated Learning Using Deep Segmentation and the Gaussian Mixture Model for Breast Cancer Tumors

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A Matlab Toolbox for Feature Importance Ranking

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Convolutional neural network and transfer learning for dose volume histogram prediction for prostate cancer radiotherapy

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An unsupervised style normalization method for cytopathology images

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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

A Study of Gene Expression, Structure, and Contractility of iPSC-Derived Cardiac Myocytes from a Family with Heart Disease due to LMNA Mutation

Mehrabi, M., Morris, T. A., Cang, Z., Nguyen, C. H. H., Sha, Y., Asad, M. N., Khachikyan, N., Greene, T. L., Becker, D., Nie, Q. H., et al. (2021) · Annals of Biomedical Engineering · doi:10.1007/s10439-021-02850-8 · Cited by 12

A Fast Hybrid Classification Algorithm with Feature Reduction for Medical Images

Mahmoud, H. A. H., AlArfaj, A. A., & Hafez, A. M. (2022) · Applied Bionics and Biomechanics · doi:10.1155/2022/1367366 · Cited by 13

Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image

Shen, W., Guo, Y., Ru, W.-E., Li, C., Zhang, G.-C., Liao, N., & Du, G. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.850515 · Cited by 12

A Decision-Making Tool for Early Detection of Breast Cancer on Mammographic Images

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Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction

Huang, K., Zhang, Y., Cheng, H.-D., & Xing, P. (2022) · Healthcare · doi:10.3390/healthcare10122480 · Cited by 13

A new two-stream network based on feature separation and complementation for ultrasound image segmentation

Zhu, Y., Li, C., Hu, K., Luo, H., Zhou, M., Li, X., & Gao, X. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104567 · Cited by 12

A cGAN-based tumor segmentation method for breast ultrasound images

You, G., Qin, Y., Zhao, C., Zhao, Y., Zhu, K., Yang, X., & Li, Y. L. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acdbb4 · Cited by 12

Age of heart disease presentation and dysmorphic nuclei in patients with LMNA mutations

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A Novel Multispace Image Reconstruction Method for Pathological Image Classification Based on Structural Information

Zhu, H., Jiang, H., Li, S., Li, H., & Pei, Y. (2019) · BioMed Research International · doi:10.1155/2019/3530903 · Cited by 12

Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images

Ferreira, M. R., Torres, H. R., Oliveira, B., Fonseca, J. G., Morais, P., Nováis, P., & Vilaça, J. L. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871091 · Cited by 12

Optimizing proportional balance between supervised and unsupervised features for ultrasound breast lesion classification

Song, M., & Kim, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105443 · Cited by 11

A deep learning-based method for the detection and segmentation of breast masses in ultrasound images

Li, W., Ye, X., Chen, X., Jiang, X., & Yang, Y. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad61b6 · Cited by 11

KRC-APM: Key region cutting and artificial prior model for breast cancer recognition in ultrasound images

Yi, L., Wang, H., & Jiang, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125092 · Cited by 11

A multi-scale, multi-task fusion UNet model for accurate breast tumor segmentation

Dai, S., Liu, X., Wei, W., Yin, X., Qiao, L., Wang, J., Zhang, Y., & Hou, Y. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108484 · Cited by 11

Making Cancer Imaging Smarter: Emerging Techniques and Computational Outlook to Guide Precision Diagnostics

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Intelligent Traffic Flow Prediction Using Deep Learning Techniques: A Comparative Study

Ahmed, S., Abdel-Hamid, Y., & Hefny, H. A. (2025) · SN Computer Science · doi:10.1007/s42979-024-03552-3 · Cited by 11

Application of artificial intelligence in the diagnosis of haematological disorders

Almbaidin, H., Abeden, Z., Alhawamdeh, M., Nazman, H. N. M., Hamdani, H. Y., Yusoff, N. M., & Isa, A. (2024) · Biomedical Research and Therapy · doi:10.15419/bmrat.v11i12.945 · Cited by 11

SAM-MedUS: a foundational model for universal ultrasound image segmentation

Tian, F., Zhai, J., Gong, J., Lei, W., Chang, S., Ju, F., Qian, S., & Zou, X. (2025) · Journal of medical imaging · doi:10.1117/1.jmi.12.2.027001 · Cited by 11

ResU-KAN: a medical image segmentation model integrating residual convolutional attention and atrous spatial pyramid pooling

Wang, H., Zhao, Z., Liu, Q., & Wang, S. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06467-5 · Cited by 12

FABRF-Net: A frequency-aware boundary and region fusion network for breast ultrasound image segmentation

Liu, Y., Yang, Y. Z., Jiang, Y., Zhao, X., & Xie, Z. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103299 · Cited by 11

Accurate classification of benign and malignant breast tumors in ultrasound imaging with an enhanced deep learning model

Liu, B.-Q., Liu, S., Cao, Z., Zhang, J., Pu, X., & Yu, J. (2025) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1526260 · Cited by 11

CR‐SSL : A closely related self‐supervised learning based approach for improving breast ultrasound tumor segmentation

Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22693 · Cited by 11

A Progressive and Cross-Domain Deep Transfer Learning Framework for Wrist Fracture Detection

Karam, C., Zini, J. E., Awad, M., Saade, C., Naffaa, L. N., & Amine, M. E. (2021) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2022-0007 · Cited by 12

Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images

Yadav, N., Dass, R., & Virmani, J. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02849-4 · Cited by 11

RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

Kabir, S. M., Bhuiyan, M. I. H., Tanveer, M. S., & Shihavuddin, A. (2021) · Applied Sciences · doi:10.3390/app112412138 · Cited by 11

Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

Tamang, L. D., & Kim, B. W. (2022) · Sensors · doi:10.3390/s22083076 · Cited by 11

Breast Infrared Thermography Segmentation Based on Adaptive Tuning of a Fully Convolutional Network

Tayel, M. B., & Elbagoury, A. M. (2020) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405615666190503142031 · Cited by 11

Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer

Wu, X., Guo, Y. F., Sa, Y., Song, Y., Li, X., Lv, Y., Xing, D., Sun, Y., Cong, Y., Yu, H., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.823897 · Cited by 10

GFA-Net: Global Feature Aggregation Network Based on Contrastive Learning for Breast Lesion Automated Segmentation in Ultrasound Images

Wang, T., Jin, C., Chen, Y., Zhou, G., Ge, R., Xue, C., Shi, B., Liu, T., Coatrieux, J.-L., & Feng, Q. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3497186 · Cited by 10

FL-PSeC: Federated Learning-Pseudo Labeled Medical Image Segmentation with Personalized Class Balancing Semi-supervised Approach

Priya, I., & Mohan, C. K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78192-6_15 · Cited by 10

Boundary-enhanced local-global collaborative network for medical image segmentation

Qiu, H., Zhong, C., Gao, C., & Huang, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-93875-9 · Cited by 10

Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging

Saini, M., Hassanzadeh, S., Musa, B., Fatemi, M., & Alizad, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-99009-5 · Cited by 10

Grounding DINO-US-SAM: Text-Prompted Multiorgan Segmentation in Ultrasound With LoRA-Tuned Vision–Language Models

Rasaee, H., Koleilat, T., & Rivaz, H. J. (2025) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2025.3605285 · Cited by 13

A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

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