ArticleQuantitative imaging in medicine and surgery2021
Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?European radiology experimental · 2024Article
- Deep-learning-based sampling position selection on color Doppler sonography images during renal artery ultrasound scanning.Scientific reports · 2024Article
- Review
- Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis.Quantitative imaging in medicine and surgery · 2023Article
- Deep learning model for the diagnosis of breast cancers smaller than 1 cm with ultrasonography: integration of ultrasonography and clinical factors.Quantitative imaging in medicine and surgery · 2023Article
- Learning curve in ultrasound-guided vacuum-assisted excision of breast lesions for surgeons and ultrasound physicians.Quantitative imaging in medicine and surgery · 2023Article
- Automated measurement of leg length discrepancy from infancy to adolescence based on cascaded LLDNet and comprehensive assessment.Quantitative imaging in medicine and surgery · 2023Article
- Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review.Diagnostics (Basel, Switzerland) · 2022Review
- Establishment of a deep-learning system to diagnose BI-RADS4a or higher using breast ultrasound for clinical application.Cancer science · 2022Article
- Application of deep learning to identify ductal carcinomaQuantitative imaging in medicine and surgery · 2022Article
- The use of deep learning technology for the detection of optic neuropathy.Quantitative imaging in medicine and surgery · 2022Review
- Identification of benign and malignant thyroid nodules based on dynamic AI ultrasound intelligent auxiliary diagnosis system.Frontiers in endocrinology · 2022Article
- Intelligent Research Based on Deep Learning Recognition Method in Vehicle-Road Cooperative Information Interaction System.Computational intelligence and neuroscience · 2022Article
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8 authors.
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Abstract
backgroundThe successful recognition of benign and malignant breast nodules using ultrasound images is based mainly on supervised learning that requires a large number of labeled images. However, because high-quality labeling is expensive and time-consuming, we hypothesized that semi-supervised learning could provide a low-cost and powerful alternative approach. This study aimed to develop an accurate semi-supervised recognition method and compared its performance with supervised methods and sonographers.
methodsThe faster region-based convolutional neural network was used for nodule detection from ultrasound images. A semi-supervised classifier based on the mean teacher model was proposed to recognize benign and malignant nodule images. The general performance of the proposed method on two datasets (8,966 nodules) was reported.
resultsThe detection accuracy was 0.88±0.03 and 0.86±0.02, respectively, on two testing sets (1,350 and 2,220 nodules). When 800 labeled training nodules were available, the proposed semi-supervised model plus 4,396 unlabeled nodules performed better than the supervised learning model (area under the curve (AUC): 0.934±0.026
conclusionsThe semi-supervised model can achieve excellent performance for nodule recognition and be useful for medical sciences. The method reduced the number of labeled images required for training, thus significantly alleviating the difficulty in data preparation of medical artificial intelligence.
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