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Visiopharm AS pre-trained deep-learning algorithm
Pre Trained Deep Learning Algorithm, supplied by Visiopharm AS, 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/deep+learning+algorithm/pre+trained+deep+learning+algorithm/pmc11144811-188-4-11
Average 90 stars, based on 1 article reviews
pre-trained deep-learning algorithm - by Bioz Stars, 2026-09
90/100 stars

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Article Title: Protocol to quantify immune cell distribution from the vasculature to the glioma microenvironment on sequential immunofluorescence multiplex images
Article Snippet: This process uses a pre-trained deep-learning algorithm commercially available within the Visiopharm Software.

Article Title: Targeting sinonasal undifferentiated carcinoma with a combinatory immunotherapy approach
Article Snippet: Tissue segmentation (tumor and stroma) was initially performed based on cell morphology, tissue structures, and CK signals, then confirmed using H&E staining as a reference after the cell detection process by using Visiopharm Deep Learning U-Net detecting DAPI to identify the cell nuclei.

Article Title: Automated annotation of virtual dual stains to generate convolutional neural network for detecting cancer metastases in H&E-stained lymph nodes.
Article Snippet: Ultimately, Visiopharm’s U-net creates a heatmap that shows the possibility of every pixel being benign or malignant.

Article Title: Digital quantification of Ki67 and PRAME in challenging melanocytic lesions - A novel diagnostic tool.
Article Snippet: The interpretation of immunohistochemical markers in melanocytic lesions possesses difficulties due to expression in non-melanocytic cells and the time-consuming, non-reproducible nature of manual assessment.. A digital tool that accurately quantifies Ki67 and PRAME may valuably aid pathologists in the diagnostic classification of melanocytic lesions.. The aim of this study was to assess the diagnostic performance of digitally quantified Ki67 and PRAME in challenging melanocytic lesions utilizing double nuclear staining methods for accurate identification of melanocytic cells.

Article Title: Protocol to quantify immune cell distribution from the vasculature to the glioma microenvironment on sequential immunofluorescence multiplex images
Article Snippet: Use the pre-trained deep-learning algorithm in the Visiopharm software. b.

Article Title: Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method
Article Snippet: The APP had initially been trained at 20× magnification leveraging a deep learning classifier (U-Net, 200,000 iterations) to identify background, nuclei, and nuclear boundary and is available as a ready-to-use APP without additional training ( https://visiopharm.com/app-center/app/nuclei-detection-ai-brightfield ).

Article Title: Immunohistochemical double nuclear staining for cell-specific automated quantification of the proliferation index - A promising diagnostic aid for melanocytic lesions.
Article Snippet: The nuclei were identified using Visiopharm’s pre-trained Deep Learning (U-net) application.



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