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Proceedings Paper • Open Access

Machine-learning with a small training set for classification of quantitative phase images of cancer cells (Conference Presentation)
Author(s): Natan T. Shaked

Paper Abstract

One of the main bottlenecks of deep learning is the requirement for many training examples. In medical imaging, these examples are not always available. I will present our latest advances in the development of machine learning classification on interferometric phase microscopy (IPM) quantitative tomographic maps to obtain grading of cancer cells without staining. We first applied principle component analysis (PCA) followed by support vector machine (SVM) classifiers. To apply deep learning with small training sets, we proposed a new deep learning method, TOP-GAN, which is a hybridization between transfer learning and generative adversarial networks.

Paper Details

Date Published: 9 March 2020
Proc. SPIE 11299, AI and Optical Data Sciences, 112990Q (9 March 2020);
Show Author Affiliations
Natan T. Shaked, Tel Aviv Univ. (Israel)

Published in SPIE Proceedings Vol. 11299:
AI and Optical Data Sciences
Bahram Jalali; Ken-ichi Kitayama, Editor(s)

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