ArticleBiomedical signal processing and control2020
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network.
Article in Biomedical signal processing and control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Comparison of breast ultrasound image classification accuracy between convolutional neural networks and human experts using multicenter external validation cohort data.Journal of medical ultrasonics (2001) · 2026Article
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- Hybrid adaptive attention deep supervision-guided U-Net for breast lesion segmentation in ultrasound computed tomography images.Medical & biological engineering & computing · 2026Article
- CDM-UNet: Content-Driven Enhanced Mamba Model for Medical Image Segmentation.Interdisciplinary sciences, computational life sciences · 2026Article
- Multimodal Cross-Attention Fusion of B-Mode Ultrasound and Strain Elastography for Tumor Segmentation in Robotics-Assisted Surgery.IEEE transactions on medical robotics and bionics · 2026Article
- Multi-level semi-supervised GAN enables accurate segmentation of breast ultrasound images.Scientific reports · 2026Article
- Breast Ultrasound Image Segmentation Using Multi-branch Skip Connection Search.Journal of imaging informatics in medicine · 2026Article
- Article
- Deep Learning for Tumor Segmentation and Multiclass Classification in Breast Ultrasound Images Using Pretrained Models.Sensors (Basel, Switzerland) · 2025Article
- A hybrid attention network for accurate breast tumor segmentation in ultrasound images.Scientific reports · 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
- When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer.Medical & biological engineering & computing · 2025Article
- Attention residual network for medical ultrasound image segmentation.Scientific reports · 2025Article
- BCT-Net: semantic-guided breast cancer segmentation on BUS.Medical & biological engineering & computing · 2025Article
- MDiagnostics (Basel, Switzerland) · 2025Article
- Dual-channel compression mapping network with fused attention mechanism for medical image segmentation.Scientific reports · 2025Article
- Enhanced breast cancer diagnosis using modified InceptionNet-V3: a deep learning approach for ultrasound image classification.Frontiers in physiology · 2025Article
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- Attention based UNet model for breast cancer segmentation using BUSI dataset.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To address this issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKs was to adjust network's receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions. The proposed method was developed and evaluated using US images collected from 882 breast masses. Moreover, we used three datasets of US images collected at different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Net achieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluated on three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improved mean Dice scores by ~6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman's rank coefficient of 0.7, between the utilization of dilated convolutions and breast mass size in the case of network's expansion path. Our study shows the usefulness of deep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weights can be found at github.com/mbyr/bus_seg.
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