ArticleNature communications2023
Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images?Medical & biological engineering & computing · 2026Article
- An Efficient Cross-Modal Interaction and Dynamic Fusion Network for Multimodal Breast Ultrasound Diagnosis.Tomography (Ann Arbor, Mich.) · 2026Article
- Dual-discriminator network-based classification method for breast ultrasound imaging.Quantitative imaging in medicine and surgery · 2026Article
- Advanced imaging techniques for tumor intraoperative navigation imaging.Npj imaging · 2026Review
- Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.Nature communications · 2026Article
- Synthesize contrast-enhanced ultrasound image of thyroid nodules via generative adversarial networks.European radiology · 2026Article
- Mechanomedicine.Nature reviews bioengineering · 2026Article
- Endoscopic ultrasound elastography in solid pancreatic lesions: individual and combined performance of strain ratio, fat-lesion strain ratio, and mean strain histogram for diagnosing pancreatic ductal adenocarcinoma.Clinical and experimental medicine · 2025Article
- Dual-modal ultrasound-based deep learning radiomics for differentiation of benign and malignant breast lesions.Gland surgery · 2025Article
- Enhancing Radiographic Diagnosis: CycleGAN-Based Methods for Reducing Cast Shadow Artifacts in Wrist Radiographs.Journal of imaging informatics in medicine · 2025Article
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Correlation between ultrasound measurements and the expression of Her-2 and CD147 in breast cancer.American journal of translational research · 2025Article
- Key parameters in intratumoral-peritumoral region fusion models: optimizing deep learning radiomics for breast cancer diagnosis.Frontiers in oncology · 2025Article
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Teacher-student guided knowledge distillation for unsupervised convolutional neural network-based speckle tracking in ultrasound strain elastography.Medical & biological engineering & computing · 2024Review
- Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.Korean journal of radiology · 2024Review
- TSE-GAN: strain elastography using generative adversarial network for thyroid disease diagnosis.Frontiers in bioengineering and biotechnology · 2024Article
- Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.Korean journal of radiology · 2023Review
- Review
- Combining conventional ultrasound and ultrasound elastography to predict HER2 status in patients with breast cancer.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
44 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.
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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.