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convolutional neural network (cnn) artificial intelligence (ai) software  (Aiforia Technologies)

 
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    Aiforia Technologies convolutional neural network (cnn) artificial intelligence (ai) software
    Convolutional Neural Network (Cnn) Artificial Intelligence (Ai) Software, supplied by Aiforia Technologies, 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/cnn+software/convolutional+neural+network++cnn++artificial+intelligence++ai++software/pm37443670-159-8-16
    Average 90 stars, based on 1 article reviews
    convolutional neural network (cnn) artificial intelligence (ai) software - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Deep neural network analysis - a paradigm shift for histological examination of health and welfare of farmed fish
    Article Snippet: Contents lists available at ScienceDirect Aquaculture journal homepage: www.elsevier.com/locate/aquaculture Deep neural network analysis - a paradigm shift for histological examination of health and welfare of farmed fish Lene Sveen⁎, Gerrit Timmerhaus, Lill-Heidi Johansen, Elisabeth Ytteborg Nofima, Muninbakken 9, 9019 Tromsø, Norway A R T I C L E I N F O Keywords: Neural network Artificial intelligence Fish skin Mucous cells Epidermis Dermis Tissue A B S T R A C T An artificial intelligence model (AI-model) was trained for the first time to detect multi-class segmentation of skin from Atlantic salmon, using a convolutional neural network (Aiforia®).

    Article Title: Core Needle Biopsy Guidance Based on Tissue Morphology Assessment With AI-OCT Imaging
    Article Snippet: Data Processing: OCT image analysis was performed using Convolutional Neural Network (CNN) artificial intelligence (AI) software, Aiforia Technologies Oyj, Pursimiehenkatu 29-31, FI-00150 Helsinki, Finland.

    Article Title: Mismatch repair deficiency is not sufficient to elicit tumor immunogenicity
    Article Snippet: Quantification of lung tumor burden by grade was performed on scans of hematoxylin and eosin (H&E)-stained sections by automated convolutional neural network (CNN)—developed in collaboration with Aiforia Technologies Oy in consultation with veterinarian pathologist R. Bronson.

    Article Title: Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging.
    Article Snippet: Data Processing: OCT image analysis was performed using Convolutional Neural Network (CNN) artificial intelligence (AI) software, Aiforia Technologies Oyj, Pursimiehenkatu 29-31, FI-00150 Helsinki, Finland.



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    Image Search Results


    Papers on AI tools applied to diagnoses of knee conditions.

    Journal: Medicina

    Article Title: Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review

    doi: 10.3390/medicina61040737

    Figure Lengend Snippet: Papers on AI tools applied to diagnoses of knee conditions.

    Article Snippet: Hoffmann et al. [ ] tested the performance of a CNN implemented in a common planning software (mediCAD ® 7.0; mediCAD Hectec GmbH) that allows analysis and preoperative planning.

    Techniques: Imaging, Diagnostic Assay, Sequencing, Software, Labeling, Biomarker Discovery, Magnetic Resonance Imaging

    Papers reviewed on AI tools applied to surgical planning.

    Journal: Medicina

    Article Title: Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review

    doi: 10.3390/medicina61040737

    Figure Lengend Snippet: Papers reviewed on AI tools applied to surgical planning.

    Article Snippet: Hoffmann et al. [ ] tested the performance of a CNN implemented in a common planning software (mediCAD ® 7.0; mediCAD Hectec GmbH) that allows analysis and preoperative planning.

    Techniques: Imaging, Diagnostic Assay, Software, Immunocytochemistry, Generated, Functional Assay, Biomarker Discovery

    Schematic description of the initial (‘out-of-box’) convolutional neural network (CNN) used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.

    Journal: BJA Open

    Article Title: Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study ☆

    doi: 10.1016/j.bjao.2023.100236

    Figure Lengend Snippet: Schematic description of the initial (‘out-of-box’) convolutional neural network (CNN) used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.

    Article Snippet: A CNN in RStudio was built based on a published example of code used to process the Modified National Institute of Standards and Technology (MNIST) image dataset of handwritten digits ( ).

    Techniques: Modification, Muscles, Activation Assay