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Classification of quantitative phase images of cancer cells using machine learning (Conference Presentation)
Author(s): Natan T. Shaked

Paper Abstract

I will present our latest advances in the development of machine learning classifier on interferometric phase microscopy (IPM) quantitative tomographic maps, obtained by new wavefront sensors, in order to obtain real-time grading of cancer cells. An internal contrast mechanism that can be used when imaging cells without staining is their refractive index. The light beam passing through the imaged cells is delayed, since the cells have a slightly higher refractive index compared with their surroundings, which can be captured by IPM. Contrary to qualitative phase contrast methods, IPM yields the full sample wavefront containing the optical thickness map or optical path delay (OPD) map of the cell, so that on each x–y point of this map, OPD is equal to the integral of the refractive index values across the cell thickness. We have recently compared the quantitative phase imaging-based features of healthy and cancer cells and of primary cancer and metastatic cancer cells under stain-free IPM. For this task, we have chosen pairs of cell lines taken from the same individual and organ. The cells were round and unattached to the surface to allow imaging during flow, and therefore most of the cells look alike, thus subjective pathological examination cannot be performed, even under IPM. We therefore applied both a new deep learning approach that can work with a small training set and a principle component analysis (PCA) followed by support vector machine (SVM) classifiers, and obtained classification results (healthy/cancer/metastatic) with over 90% sensitivity and specificity.

Paper Details

Date Published: 10 September 2019
PDF
Proc. SPIE 11139, Applications of Machine Learning, 111390Z (10 September 2019); doi: 10.1117/12.2530990
Show Author Affiliations
Natan T. Shaked, Tel Aviv Univ. (Israel)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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