ArticleUltrasound in medicine & biology2020
Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images.
Article in Ultrasound in medicine & biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.Physics in medicine and biology · 2021Trial
- 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
- Integrating DNA methylation biomarkers for breast cancer risk prediction using artificial intelligence.Scientific reports · 2026Article
- Modifying the U-Net's Encoder-Decoder Architecture for Segmentation of Tumors in Breast Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.Current medicinal chemistry · 2026Article
- Diffuse optical imaging with channel attention fusion network.Journal of biomedical optics · 2026Article
- SEED-Net: statistical echo evidence and detail modeling for ultrasound lesion classification across multiple organ-specific datasets.Frontiers in medicine · 2026Article
- Enhancing deep learning interpretability for hand-crafted feature-guided histologic image classification via weak-to-strong generalization.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- [A joint distillation model for the tumor segmentation using breast ultrasound images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.BMC medical imaging · 2025Article
- HMA-Net: a hybrid mixer framework with multihead attention for breast ultrasound image segmentation.Frontiers in artificial intelligence · 2025Article
- Article
- Expansive Receptive Field and Local Feature Extraction Network: Advancing Multiscale Feature Fusion for Breast Fibroadenoma Segmentation in Sonography.Journal of imaging informatics in medicine · 2024Article
- Enhancing Surgical Guidance: Deep Learning-Based Liver Vessel Segmentation in Real-Time Ultrasound Video Frames.Cancers · 2024Article
- Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour.Scientific reports · 2024Article
- A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception.Current oncology (Toronto, Ont.) · 2024Article
- A domain knowledge-based interpretable deep learning system for improving clinical breast ultrasound diagnosis.Communications medicine · 2024Article
- Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images.Visual computing for industry, biomedicine, and art · 2024Article
- Multiview deep learning networks based on automated breast volume scanner images for identifying breast cancer in BI-RADS 4.Frontiers in oncology · 2024Article
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3 authors.
Funding
Abstract
Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach to integrating visual saliency into a deep learning model for breast tumor segmentation in ultrasound images. Visual saliency refers to image maps containing regions that are more likely to attract radiologists' visual attention. The proposed approach introduces attention blocks into a U-Net architecture and learns feature representations that prioritize spatial regions with high saliency levels. The validation results indicate increased accuracy for tumor segmentation relative to models without salient attention layers. The approach achieved a Dice similarity coefficient (DSC) of 90.5% on a data set of 510 images. The salient attention model has the potential to enhance accuracy and robustness in processing medical images of other organs, by providing a means to incorporate task-specific knowledge into deep learning architectures.
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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.