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2d and 3d pretrained feature encoders based on convolutional neural networks (cnn) or vision transformer (vit)  (TriPath Inc)

 
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    TriPath Inc 2d and 3d pretrained feature encoders based on convolutional neural networks (cnn) or vision transformer (vit)
    2d And 3d Pretrained Feature Encoders Based On Convolutional Neural Networks (Cnn) Or Vision Transformer (Vit), supplied by TriPath Inc, 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/3d+encoder/2d+and+3d+pretrained+feature+encoders+based+on+convolutional+neural+networks++cnn++or+vision+transformer++vit+/pm38729110-1038-8-0
    Average 90 stars, based on 1 article reviews
    2d and 3d pretrained feature encoders based on convolutional neural networks (cnn) or vision transformer (vit) - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Analysis of 3D pathology samples using weakly supervised AI.
    Article Snippet: TriPath provides the choice between a range of 2D and 3D pretrained feature encoders based on convolutional neural networks (CNN) or vision transformer (ViT)110 for transfer learning.



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    Ablation study: performance measures ± standard error of the mean. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark$\end{document} ’ indicates included, ‘×’ indicates excluded model components; the first two rows are the complete CANDELA model with the two different pre-training stragies. Models marked ‘ † ’ are significantly worse than CANDELA using only one pre-training task. Models marked ‘*’ are significantly better (pairwise t -test, α = 0.05, corrected for multiple testing using Holm–Šídák). Bold values indicate the best configuration

    Journal: NAR Genomics and Bioinformatics

    Article Title: Cancer drug sensitivity estimation using modular deep Graph Neural Networks

    doi: 10.1093/nargab/lqae043

    Figure Lengend Snippet: Ablation study: performance measures ± standard error of the mean. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark$\end{document} ’ indicates included, ‘×’ indicates excluded model components; the first two rows are the complete CANDELA model with the two different pre-training stragies. Models marked ‘ † ’ are significantly worse than CANDELA using only one pre-training task. Models marked ‘*’ are significantly better (pairwise t -test, α = 0.05, corrected for multiple testing using Holm–Šídák). Bold values indicate the best configuration

    Article Snippet: As an alternative to pre-training the drug encoder on matabolite properties and toxicity, we have studied the use of the 3D Infomax encoding for drugs.

    Techniques: