Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening

• URL: http://arxiv.org/abs/2010.01173v1
• Date: Fri, 2 Oct 2020 19:17:07 GMT
• Title: Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening
• Authors: Sumeet Menon, David Chapman, Phuong Nguyen, Yelena Yesha, Michael Morris, Babak Saboury
• Abstract summary: We present a semi-supervised algorithm for lung cancer screening. A 3D Convolutional Neural Network (CNN) is trained using the Expectation-Maximization (EM) meta-algorithm. We show that the Semi-Supervised EM algorithm greatly improves the classification accuracy of the cross-domain lung cancer screening.
• Score: 1.5379084885764847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a semi-supervised algorithm for lung cancer screening in which a 3D Convolutional Neural Network (CNN) is trained using the Expectation-Maximization (EM) meta-algorithm. Semi-supervised learning allows a smaller labelled data-set to be combined with an unlabeled data-set in order to provide a larger and more diverse training sample. EM allows the algorithm to simultaneously calculate a maximum likelihood estimate of the CNN training coefficients along with the labels for the unlabeled training set which are defined as a latent variable space. We evaluate the model performance of the Semi-Supervised EM algorithm for CNNs through cross-domain training of the Kaggle Data Science Bowl 2017 (Kaggle17) data-set with the National Lung Screening Trial (NLST) data-set. Our results show that the Semi-Supervised EM algorithm greatly improves the classification accuracy of the cross-domain lung cancer screening, although results are lower than a fully supervised approach with the advantage of additional labelled data from the unsupervised sample. As such, we demonstrate that Semi-Supervised EM is a valuable technique to improve the accuracy of lung cancer screening models using 3D CNNs.

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