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      Biosensing by Learning: Cancer Detection as Iterative optimization.

      Chen, Yifan; Sharifi, Neda; Holmes, Geoffrey; Cheang, U. Kei
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      EMBC18_0483_MS-1.pdf
      Submitted version, 422.1Kb
      DOI
       10.1109/EMBC.2018.8512705
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      Chen, 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.2018.8512705
      Permanent Research Commons link: https://hdl.handle.net/10289/13039
      Abstract
      We 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.
      Date
      2018
      Type
      Conference Contribution
      Publisher
      IEEE
      Rights
      This 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.
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      • Computing and Mathematical Sciences Papers [1454]
      • Science and Engineering Papers [3084]
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