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Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells

Wan, Jiachen; Dong, Yang; Xue, Jing-Hao; Lin, Liyan; Du, Shan; Dong, Jia; Yao, Yue; ... Ma, Hui; + view all (2022) Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells. Biomedical Optics Express , 13 (6) pp. 3339-3354. 10.1364/boe.456649. Green open access

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Abstract

We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening.

Type: Article
Title: Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells
Open access status: An open access version is available from UCL Discovery
DOI: 10.1364/boe.456649
Publisher version: https://doi.org/10.1364/boe.456649
Language: English
Additional information: Copyright © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement (https://doi.org/10.1364/OA_License_v2#VOR-OA).
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10149024
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