ReviewFrontiers in oncology2023
Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound.
Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- A Deep Learning Approach for Classifying Benign, Malignant, and Borderline Ovarian Tumors Using Convolutional Neural Networks and Generative Adversarial Networks.Medical sciences (Basel, Switzerland) · 2026Article
- Treatment of recurrent hepatocellular carcinoma: The current standards and future perspectives.World journal of gastrointestinal oncology · 2025Review
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation.BMC medical imaging · 2025Article
- Deep learning-based CT-free attenuation correction for cardiac SPECT: a new approach.BMC medical imaging · 2025Article
- Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.BMC medical imaging · 2025Article
- Ultrasound elastography: advances and challenges in early detection of breast cancer.Frontiers in oncology · 2025Review
- Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images.BMC medical imaging · 2024Article
- The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results.BMC medical imaging · 2024Article
- T1 mapping-based radiomics in the identification of histological types of lung cancer: a reproducibility and feasibility study.BMC medical imaging · 2024Article
- Spatial attention-based CSR-Unet framework for subdural and epidural hemorrhage segmentation and classification using CT images.BMC medical imaging · 2024Article
- Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings.BMC medical imaging · 2024Article
- The value evaluation of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection.BMC medical imaging · 2024Article
- Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.BMC medical imaging · 2024Article
- Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy.BMC medical imaging · 2024Article
- An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images.BMC medical imaging · 2024Article
- A deep learning-powered diagnostic model for acute pancreatitis.BMC medical imaging · 2024Article
- Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions.Frontiers in cardiovascular medicine · 2024Article
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
Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.
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Registered trials
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.