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Tamas Balassa

Showing results (1-10 of 9) with videos related to

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Cell Systems|June 29, 2018
Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based AssaysKevin Smith, Filippo Piccinini, Tamas Balassa, et al.
Scientific Reports|August 29, 2018
A deep convolutional neural network approach for astrocyte detectionIlida Suleymanova, Tamas Balassa, Sushil Tripathi, et al.
Computational and Structural Biotechnology Journal|July 3, 2020
Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregatesFilippo Piccinini, Tamas Balassa, Antonella Carbonaro, et al.
Scientific Reports|July 6, 2018
Environmental properties of cells improve machine learning-based phenotype recognition accuracyTimea Toth, Tamas Balassa, Norbert Bara, et al.
Cell Systems|June 26, 2017
Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging DataFilippo Piccinini, Tamas Balassa, Abel Szkalisity, et al.
Nature Communications|February 11, 2021
Automatic deep learning-driven label-free image-guided patch clamp systemKrisztian Koos, Gáspár Oláh, Tamas Balassa, et al.
Nature Communications|May 6, 2021
Regression plane concept for analysing continuous cellular processes with machine learningAbel Szkalisity, Filippo Piccinini, Attila Beleon, et al.
Nature Communications|January 17, 2018
Intelligent image-based in situ single-cell isolationCsilla Brasko, Kevin Smith, Csaba Molnar, et al.
Cell Systems|July 5, 2021
nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style TransferReka Hollandi, Abel Szkalisity, Timea Toth, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Cell Systems|June 29, 2018
Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based AssaysKevin Smith, Filippo Piccinini, Tamas Balassa, et al.
Scientific Reports|August 29, 2018
A deep convolutional neural network approach for astrocyte detectionIlida Suleymanova, Tamas Balassa, Sushil Tripathi, et al.
Computational and Structural Biotechnology Journal|July 3, 2020
Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregatesFilippo Piccinini, Tamas Balassa, Antonella Carbonaro, et al.
Scientific Reports|July 6, 2018
Environmental properties of cells improve machine learning-based phenotype recognition accuracyTimea Toth, Tamas Balassa, Norbert Bara, et al.
Cell Systems|June 26, 2017
Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging DataFilippo Piccinini, Tamas Balassa, Abel Szkalisity, et al.
Nature Communications|February 11, 2021
Automatic deep learning-driven label-free image-guided patch clamp systemKrisztian Koos, Gáspár Oláh, Tamas Balassa, et al.
Nature Communications|May 6, 2021
Regression plane concept for analysing continuous cellular processes with machine learningAbel Szkalisity, Filippo Piccinini, Attila Beleon, et al.
Nature Communications|January 17, 2018
Intelligent image-based in situ single-cell isolationCsilla Brasko, Kevin Smith, Csaba Molnar, et al.
Cell Systems|July 5, 2021
nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style TransferReka Hollandi, Abel Szkalisity, Timea Toth, et al.
Pageof 1