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dc.contributor.authorChen, Yifanen_NZ
dc.contributor.authorSharifi, Nedaen_NZ
dc.contributor.authorHolmes, Geoffreyen_NZ
dc.contributor.authorCheang, U. Keien_NZ
dc.coverage.spatialUnited Statesen_NZ
dc.date.accessioned2019-10-24T22:43:10Z
dc.date.available2018-07en_NZ
dc.date.available2019-10-24T22:43:10Z
dc.date.issued2018en_NZ
dc.identifier.citationChen, Y., Sharifi, N., Holmes, G., & Cheang, U. K. (2018). Biosensing by Learning: Cancer Detection as Iterative optimization. In Proceedings of 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Vol. 2018, pp. 1837–1840). Washington, DC, USA: IEEE. https://doi.org/10.1109/EMBC.2018.8512705en
dc.identifier.issn1557-170Xen_NZ
dc.identifier.urihttps://hdl.handle.net/10289/13039
dc.description.abstractWe propose a novel cancer detection procedure (CDP) based on an iterative optimization method. The global minimum of a tumor-induced biological cost function indicates the tumor location, the domain of the cost function is the tissue region at high risk of malignancy, and the time-variant guess input is a swarm of externally controllable and trackable nanorobots for tumor sensing. We consider the spatial distrib-ution of fibrin as the cost function; the fibrin is formed during the coagulation cascade activated by tumor-targeted signalling modules (nanoparticles) and recruits clot-targeted receiving modules (nanorobots) towards the site of disease. Subsequently, the CDP can be interpreted from the iterative optimization perspective: the guess input (i.e., a swarm of nanorobots) is continuously updated according to the gradient of the cost function in order to find the optimum (i.e., cancer) by moving through the domain (i.e., tissue under screening). Along this line of thought, we consider the gradient descent (GD) iterative method, and propose the GD-inspired CDP, which takes into account the realistic in vivo propagation scenario of nanorobots. Finally, we present numerical examples to demonstrate the features of the GD-inspired CDP.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEE
dc.rightsThis is an author’s submitted version of an article published in the Proceedings of 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). © 2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.subjectAlgorithmsen_NZ
dc.subjectBlood Coagulationen_NZ
dc.subjectHumansen_NZ
dc.subjectNanoparticlesen_NZ
dc.subjectNeoplasmsen_NZ
dc.subjectThrombosisen_NZ
dc.subjectMachine learning
dc.subjectMachine learning
dc.titleBiosensing by Learning: Cancer Detection as Iterative optimization.en_NZ
dc.typeConference Contribution
dc.identifier.doi10.1109/EMBC.2018.8512705en_NZ
dc.relation.isPartOfProceedings of 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)en_NZ
pubs.begin-page1837
pubs.elements-id229722
pubs.end-page1840
pubs.place-of-publicationWashington, DC, USA
pubs.publication-statusPublisheden_NZ
pubs.volume2018en_NZ


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