ArticleFrontiers in oncology2022
Ultrasound Image Classification of Thyroid Nodules Based on Deep Learning.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Artificial Intelligence in Thyroid Nodules: A Systematic Review Based on Bibliometric Analysis.Endocrine, metabolic & immune disorders drug targets · 2024Pooled it
- Deep learning for coronary arteries identification and measurement in pediatric echocardiography.Pediatric research · 2026Article
- RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration.Scientific reports · 2026Article
- Deep Learning Based on B-Mode and Color Doppler Ultrasound for Differentiation of Primary Thyroid Lymphoma and Hashimoto's Thyroiditis: A Retrospective Single-Center Study.Diagnostics (Basel, Switzerland) · 2026Article
- AI-assisted radiomics for classification of benign and non-benign right heart masses in 2D-echocardiography.Journal of translational medicine · 2026Article
- Deep learning-based classification of thyroid nodules using uncertainty-aware multi-modal ultrasound imaging.Scientific reports · 2026Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- Diagnostic performance of multimodal ultrasound-based deep learning models in differentiating benign and malignant thyroid nodules.Frontiers in oncology · 2026Article
- A Thyroid Nodule Ultrasound Image Grading Model Integrating Medical Prior Knowledge.Journal of imaging informatics in medicine · 2025Article
- Prediction of lymph node metastasis in papillary thyroid carcinoma using non-contrast CT-based radiomics and deep learning with thyroid lobe segmentation: A dual-center study.European journal of radiology open · 2025Article
- Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.BMC medical imaging · 2025Article
- Differentially localized protein identification for breast cancer based on deep learning in immunohistochemical images.Communications biology · 2024Article
- Toward Smart, Automated Junctional Tourniquets-AI Models to Interpret Vessel Occlusion at Physiological Pressure Points.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning models for interpretation of point of care ultrasound in military working dogs.Frontiers in veterinary science · 2024Article
- Explainable Automated TI-RADS Evaluation of Thyroid Nodules.Sensors (Basel, Switzerland) · 2023Article
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- Patient-level thyroid cancer classification using attention multiple instance learning on fused multi-scale ultrasound image features.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
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8 authors.
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Abstract
A thyroid nodule, which is defined as abnormal growth of thyroid cells, indicates excessive iodine intake, thyroid degeneration, inflammation, and other diseases. Although thyroid nodules are always non-malignant, the malignancy likelihood of a thyroid nodule grows steadily every year. In order to reduce the burden on doctors and avoid unnecessary fine needle aspiration (FNA) and surgical resection, various studies have been done to diagnose thyroid nodules through deep-learning-based image recognition analysis. In this study, to predict the benign and malignant thyroid nodules accurately, a novel deep learning framework is proposed. Five hundred eight ultrasound images were collected from the Third Hospital of Hebei Medical University in China for model training and validation. First, a ResNet18 model, pretrained on ImageNet, was trained by an ultrasound image dataset, and a random sampling of training dataset was applied 10 times to avoid accidental errors. The results show that our model has a good performance, the average area under curve (AUC) of 10 times is 0.997, the average accuracy is 0.984, the average recall is 0.978, the average precision is 0.939, and the average F1 score is 0.957. Second, Gradient-weighted Class Activation Mapping (Grad-CAM) was proposed to highlight sensitive regions in an ultrasound image during the learning process. Grad-CAM is able to extract the sensitive regions and analyze their shape features. Based on the results, there are obvious differences between benign and malignant thyroid nodules; therefore, shape features of the sensitive regions are helpful in diagnosis to a great extent. Overall, the proposed model demonstrated the feasibility of employing deep learning and ultrasound images to estimate benign and malignant thyroid nodules.
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