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3d cnn with a u-net like encoder–decoder architecture  (Jung Diagnostics GmbH)

 
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    Jung Diagnostics GmbH 3d cnn with a u-net like encoder–decoder architecture
    3d Cnn With A U Net Like Encoder–Decoder Architecture, supplied by Jung Diagnostics GmbH, 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/encoder-decoder+cnns/3d+cnn+with+a+u+net+like+encoder+decoder+architecture/pm37454342-57-13-21
    Average 90 stars, based on 1 article reviews
    3d cnn with a u-net like encoder–decoder architecture - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis
    Article Snippet: A 3D CNN with a U-Net like encoder–decoder architecture (Fig. ) was externally developed by jung diagnostics GmbH, Hamburg, Germany, and provided for external validation on our dataset.

    Article Title: AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.
    Article Snippet: Deep learning framework and external training A 3D CNN with a U-Net like encoder–decoder architecture (Fig. 1) was externally developed by jung diagnostics GmbH, Hamburg, Germany, and provided for external validation on our dataset.



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    3d cnn with a u-net like encoder–decoder architecture  (Jung Diagnostics GmbH)
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    encoder-decoder cnns  (European XFEL GmbH)
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    ( A – E ) Comparing input distributions to the CNN predictions reconstructed from the predicted PCA component coefficients. 5 examples were chosen from the 1000 test distributions and show the <t>CNN’s</t> best prediction ( A ), worst prediction ( E ), and a uniformly spaced range between ( B – D ). ( F ) The CNN’s prediction is shown for an experimentally measured beam output and compared to the experimentally measured beam input. ( G ) Results of the prediction from ( F ) fine tuned via ES.
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    encoder-decoder cnns  (Wolters Kluwer Health)
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    ( A – E ) Comparing input distributions to the CNN predictions reconstructed from the predicted PCA component coefficients. 5 examples were chosen from the 1000 test distributions and show the <t>CNN’s</t> best prediction ( A ), worst prediction ( E ), and a uniformly spaced range between ( B – D ). ( F ) The CNN’s prediction is shown for an experimentally measured beam output and compared to the experimentally measured beam input. ( G ) Results of the prediction from ( F ) fine tuned via ES.
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    ( A – E ) Comparing input distributions to the CNN predictions reconstructed from the predicted PCA component coefficients. 5 examples were chosen from the 1000 test distributions and show the CNN’s best prediction ( A ), worst prediction ( E ), and a uniformly spaced range between ( B – D ). ( F ) The CNN’s prediction is shown for an experimentally measured beam output and compared to the experimentally measured beam input. ( G ) Results of the prediction from ( F ) fine tuned via ES.

    Journal: Scientific Reports

    Article Title: An adaptive approach to machine learning for compact particle accelerators

    doi: 10.1038/s41598-021-98785-0

    Figure Lengend Snippet: ( A – E ) Comparing input distributions to the CNN predictions reconstructed from the predicted PCA component coefficients. 5 examples were chosen from the 1000 test distributions and show the CNN’s best prediction ( A ), worst prediction ( E ), and a uniformly spaced range between ( B – D ). ( F ) The CNN’s prediction is shown for an experimentally measured beam output and compared to the experimentally measured beam input. ( G ) Results of the prediction from ( F ) fine tuned via ES.

    Article Snippet: Recently encoder-decoder CNNs have also been demonstrated with measured beam data at the European XFEL to provide extremely high accuracy (768 × 1064 pixel images) predictions of the beam’s LPS and have also demonstrated an innovative method in which once the decoder half is trained and fixed, multiple different encoders can be used for various working points without having to re-train the decoder .

    Techniques: