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dc.contributor.authorSergioli, Giuseppe
dc.contributor.authorMilitello, Carmelo
dc.contributor.authorRundo, Leonardo
dc.contributor.authorMinafra, Luigi
dc.contributor.authorTorrisi, Filippo
dc.contributor.authorRusso, Giorgio
dc.contributor.authorChow, Keng Loon
dc.contributor.authorGiuntini, Roberto
dc.date.accessioned2021-02-02T17:14:13Z
dc.date.available2021-02-02T17:14:13Z
dc.date.issued2021-02-02
dc.date.submitted2020-08-27
dc.identifier.others41598-021-82085-8
dc.identifier.other82085
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317025
dc.description.abstractAbstract: Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity. Another approach of combining quantum information theory with machine learning—without involving quantum computers—is known as Quantum-inspired Machine Learning (QiML), which exploits the expressive power of the quantum language to increase the accuracy of the process (rather than reducing the time complexity). In this work, we propose a large-scale experiment based on the application of a binary classifier inspired by quantum information theory to the biomedical imaging context in clonogenic assay evaluation to identify the most discriminative feature, allowing us to enhance cell colony segmentation. This innovative approach offers a two-fold result: (1) among the extracted and analyzed image features, homogeneity is shown to be a relevant feature in detecting challenging cell colonies; and (2) the proposed quantum-inspired classifier is a novel and outstanding methodology, compared to conventional machine learning classifiers, for the evaluation of clonogenic assays.
dc.languageen
dc.publisherNature Publishing Group UK
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArticle
dc.subject/639/705/117
dc.subject/631/80/2373
dc.subject/639/766/483/481
dc.subjectarticle
dc.titleA quantum-inspired classifier for clonogenic assay evaluations
dc.typeArticle
dc.date.updated2021-02-02T17:14:12Z
prism.issueIdentifier1
prism.publicationNameScientific Reports
prism.volume11
dc.identifier.doi10.17863/CAM.64136
dcterms.dateAccepted2021-01-15
rioxxterms.versionofrecord10.1038/s41598-021-82085-8
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn2045-2322


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)