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u-net 1  (HighRes Biosolutions)

 
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    HighRes Biosolutions u-net 1
    U Net 1, supplied by HighRes Biosolutions, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/u-net+1/u+net/pm40390053-157-0-18
    Average 90 stars, based on 1 article reviews
    u-net 1 - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution.
    Article Snippet: U-Net 1 (17%) 1 (17%) 2 (33%) 2 (33%) Flex-Net 1 (17%) 1 (17%) 3 (49%) 1 (17%) Highres-Net 2 (33%) 3 (50%) 1 (17%) 0 (0%) Best Selection 2 (33%) 3 (50%) 1 (17%) 0 (0%) Esophageal carcinoma (n = 15) U-Net 2 (13%) 7 (47%) 5 (33%) 1 (7%) Flex-Net 2 (13%) 5 (33%) 3 (21%) 5 (33%) Highres-Net 3 (20%) 3 (20%) 4 (27%) 5 (33%) Best Selection 5 (33%) 4 (27%) 5 (33%) 1 (7%)



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    Image Search Results


    Results of the receiver operating characteristic analysis to predict 6-month progression-free survival by imaging (MRI, PET, and MRI-learned PET from U-Net 4 ) and clinical variables.

    Journal: Frontiers in Neurology

    Article Title: Feasibility of Multimodal MRI-Based Deep Learning Prediction of High Amino Acid Uptake Regions and Survival in Patients With Glioblastoma

    doi: 10.3389/fneur.2019.01305

    Figure Lengend Snippet: Results of the receiver operating characteristic analysis to predict 6-month progression-free survival by imaging (MRI, PET, and MRI-learned PET from U-Net 4 ) and clinical variables.

    Article Snippet: To investigate the effect of different multimodal MRI protocols on the performance of the U-Net system, three different U-Net systems (U-Net 1(Siemens) trained by multimodal data of Siemens Protocol, U-Net 2(Philips) trained by multimodal data of Philips Protocol, and U-Net 3 trained by combined multimodal [Siemens+Philips] Protocol) were separately implemented using Google TensorFlow library ( www.tensorflow.org ).

    Techniques: Imaging