ArticleScientific reports2021
Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- CT-based deep learning radiomics predicts micropapillary/solid components in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- GAN-generated target reconstruction CT radiomics for prediction of the novel IASLC grading of pulmonary adenocarcinoma.Journal of thoracic disease · 2026Article
- Deep learning 3D super-resolution radiomics model based on Gd-enhanced MRI for improving preoperative prediction of HCC pathological grading.Abdominal radiology (New York) · 2026Article
- Super-resolution ultrasound radiomics for pre-FNA prediction of nondiagnostic (Bethesda I) thyroid nodules.Frontiers in endocrinology · 2026Article
- Multi-Radiomics Features of Super-Resolution MRI for Predicting Minor Amputation in Wagner 3 Diabetic Foot Ulcers.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Assessing AI-augmented training for multiple sclerosis classification in a basal ganglia radiomics model.BMC medical imaging · 2025Article
- Multi-omics prediction of axillary treatment response and tumour microenvironment alterations in lymph node-positive luminal breast cancer.Cell death & disease · 2025Article
- Deep learning-based super-resolution US radiomics to differentiate testicular seminoma and non-seminoma: an international multicenter study.Insights into imaging · 2025Article
- The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy.Reproductive biology and endocrinology : RB&E · 2025Article
- Attention mechanism-based habitat analysis for predicting pleural invasion and prognosis of pulmonary nodules.Translational lung cancer research · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.Journal of imaging informatics in medicine · 2025Article
- CT-based 3D Super-resolution Radiomics for the Differential Diagnosis of BrucellaCurrent medical imaging · 2025Article
- Nomogram for predicting cervical lymph node metastasis of papillary thyroid carcinoma using deep learning-based super-resolution ultrasound image.Discover oncology · 2024Article
- Retinal fundus image super-resolution based on generative adversarial network guided with vascular structure prior.Scientific reports · 2024Article
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
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- MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.Scientific reports · 2024Article
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- Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection.Brain sciences · 2024Article
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6 authors.
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Abstract
Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore, we propose a Generative Adversarial Network (GAN)-based lesion-focused framework for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GAN-Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). At [Formula: see text] SR, the proposed model achieved better perceptual quality with less blurring than the other considered state-of-the-art SR methods, while producing comparable results at [Formula: see text] SR. We also evaluated the robustness of our model's radiomic feature in terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCA-based analysis were the most robust features extracted on the GAN-super-resolved images. These achievements pave the way for the application of GAN-based image Super-Resolution techniques for studies of radiomics for robust biomarker discovery.
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