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deep learning model thyroid nodules classification  (Lunit Inc)

 
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    Lunit Inc deep learning model thyroid nodules classification
    Deep Learning Model Thyroid Nodules Classification, supplied by Lunit Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/classification+model/classification+deep+identification+learning+model+nodules+thyroid/pm41673142-331-62-28
    Average 86 stars, based on 1 article reviews
    deep learning model thyroid nodules classification - by Bioz Stars, 2026-11
    86/100 stars

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    Related Articles

    Magnetic Resonance Imaging:

    Article Title: Performance of adult-trained artificial intelligence models in paediatric imaging-a scoping review.
    Article Snippet: .. Lhermitte, 2024 Neuro MRI (Brain, T1-w) U-Net, Pretrained Res-U-Net, nnU-Net models Stroke lesions Segmentation of stroke lesions on the MRI brain Shin, 2024** Chest Radiography (Chest) COMMERCIAL PRODUCT Lunit Insight for Chest Radiography, v3.1.2 Four pathologies: pneumothorax, consolidation, nodule, pleural effusion Detection and classification— detecting and scoring the presence of thoracic abnormalities Ha, 2025 Thyroid Ultrasound (Thyroid) COMMERCIAL PRODUCT AI-Thyroid software, a Deep Learning model thyroid nodules Classification—identification of malignant thyroid nodules Thibodeau- Antonacci, 2025 Whole body (organs at risk) CT COMMERCIAL PRODUCT LimbusAI v1.7 deep learning-based segmentation tool developed specifically for radiotherapy purposes The Organs at risk (OARs) included: the clinical target volume of the spinal canal, vertebral bodies, brain, brainstem, eyes, lenses, optic nerves, chiasm, cochleae, oesophagus, lungs, and kidneys. ..

    Software:

    Article Title: Performance of adult-trained artificial intelligence models in paediatric imaging-a scoping review.
    Article Snippet: .. Lhermitte, 2024 Neuro MRI (Brain, T1-w) U-Net, Pretrained Res-U-Net, nnU-Net models Stroke lesions Segmentation of stroke lesions on the MRI brain Shin, 2024** Chest Radiography (Chest) COMMERCIAL PRODUCT Lunit Insight for Chest Radiography, v3.1.2 Four pathologies: pneumothorax, consolidation, nodule, pleural effusion Detection and classification— detecting and scoring the presence of thoracic abnormalities Ha, 2025 Thyroid Ultrasound (Thyroid) COMMERCIAL PRODUCT AI-Thyroid software, a Deep Learning model thyroid nodules Classification—identification of malignant thyroid nodules Thibodeau- Antonacci, 2025 Whole body (organs at risk) CT COMMERCIAL PRODUCT LimbusAI v1.7 deep learning-based segmentation tool developed specifically for radiotherapy purposes The Organs at risk (OARs) included: the clinical target volume of the spinal canal, vertebral bodies, brain, brainstem, eyes, lenses, optic nerves, chiasm, cochleae, oesophagus, lungs, and kidneys. ..



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    Image Search Results


    Confusion matrix plot (Baidu Wenxin ERNIE).

    Journal: Frontiers in Medicine

    Article Title: Research on predictive model for tracheal tube sizes in adult double-lumen endotracheal intubation based on radiomics and artificial intelligence

    doi: 10.3389/fmed.2025.1657138

    Figure Lengend Snippet: Confusion matrix plot (Baidu Wenxin ERNIE).

    Article Snippet: To conduct a more precise and comprehensive analysis of the predictive performance of the Baidu Wenxin ERNIE model across different classification scenarios, this study employed a multidimensional analytical approach.

    Techniques:

    Ranking of radiomics features (Baidu Wenxin ERNIE).

    Journal: Frontiers in Medicine

    Article Title: Research on predictive model for tracheal tube sizes in adult double-lumen endotracheal intubation based on radiomics and artificial intelligence

    doi: 10.3389/fmed.2025.1657138

    Figure Lengend Snippet: Ranking of radiomics features (Baidu Wenxin ERNIE).

    Article Snippet: To conduct a more precise and comprehensive analysis of the predictive performance of the Baidu Wenxin ERNIE model across different classification scenarios, this study employed a multidimensional analytical approach.

    Techniques: