ArticlePloS one2019
An RDAU-NET model for lesion segmentation in breast ultrasound images.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 208 citations in OpenAlex.
- Five-Year Breast Cancer Risk Prediction From Screening Breast Ultrasound Using Deep Learning.medRxiv : the preprint server for health sciences · 2026Article
- Multi-level semi-supervised GAN enables accurate segmentation of breast ultrasound images.Scientific reports · 2026Article
- Inception U-Net for Enhanced Breast Ultrasound Image Segmentation Using Transfer Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Rolling shutter-resistant confocal endomicroscopy image stitching via dual-path Gaussian U-Net.Journal of biomedical optics · 2025Article
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation.Journal of imaging informatics in medicine · 2025Article
- Artificial intelligence-enhanced handheld breast ultrasound for screening: A systematic review of diagnostic test accuracy.PLOS digital health · 2025Article
- Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction.Quantitative imaging in medicine and surgery · 2024Article
- Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images.Diagnostics (Basel, Switzerland) · 2024Article
- Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images.Journal of imaging · 2024Article
- Encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs.PeerJ. Computer science · 2024Article
- DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.PloS one · 2024Article
- Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images.PloS one · 2024Article
- BUS-Net: Breast Tumour Detection Network for Ultrasound Images Using Bi-directional ConvLSTM and Dense Residual Connections.Journal of digital imaging · 2023Article
- Fully automatic tumor segmentation of breast ultrasound images with deep learning.Journal of applied clinical medical physics · 2023Article
- HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2022Article
- ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation.Healthcare (Basel, Switzerland) · 2022Article
- Dilated transformer: residual axial attention for breast ultrasound image segmentation.Quantitative imaging in medicine and surgery · 2022Article
- BUSIS: A Benchmark for Breast Ultrasound Image Segmentation.Healthcare (Basel, Switzerland) · 2022Review
- EMT-NET: EFFICIENT MULTITASK NETWORK FOR COMPUTER-AIDED DIAGNOSIS OF BREAST CANCER.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a common gynecological disease that poses a great threat to women health due to its high malignant rate. Breast cancer screening tests are used to find any warning signs or symptoms for early detection and currently, Ultrasound screening is the preferred method for breast cancer diagnosis. The localization and segmentation of the lesions in breast ultrasound (BUS) images are helpful for clinical diagnosis of the disease. In this paper, an RDAU-NET (Residual-Dilated-Attention-Gate-UNet) model is proposed and employed to segment the tumors in BUS images. The model is based on the conventional U-Net, but the plain neural units are replaced with residual units to enhance the edge information and overcome the network performance degradation problem associated with deep networks. To increase the receptive field and acquire more characteristic information, dilated convolutions were used to process the feature maps obtained from the encoder stages. The traditional cropping and copying between the encoder-decoder pipelines were replaced by the Attention Gate modules which enhanced the learning capabilities through suppression of background information. The model, when tested with BUS images with benign and malignant tumor presented excellent segmentation results as compared to other Deep Networks. A variety of quantitative indicators including Accuracy, Dice coefficient, AUC(Area-Under-Curve), Precision, Sensitivity, Specificity, Recall, F1score and M-IOU (Mean-Intersection-Over-Union) provided performances above 80%. The experimental results illustrate that the proposed RDAU-NET model can accurately segment breast lesions when compared to other deep learning models and thus has a good prospect for clinical diagnosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.