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.
@article{arxiv.2010.01173,
title = {Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening},
author = {Sumeet Menon and David Chapman and Phuong Nguyen and Yelena Yesha and Michael Morris and Babak Saboury},
journal= {arXiv preprint arXiv:2010.01173},
year = {2020}
}
Comments
This paper has been accepted at the ACM SIGKDD Workshop DCCL 2019. https://sites.google.com/view/kdd-workshop-2019/accepted-papers https://drive.google.com/file/d/0B8FX-5qN3tbjM3c4SVZDYWxjbGhCekhjUV9PUC11b3dOSXRR/view