Proceedings Paper • Open AccessMachine-learning with a small training set for classification of quantitative phase images of cancer cells (Conference Presentation)
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.