# Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening

arXiv: https://arxiv.org/abs/2010.01173 · HTML: https://mlsanity.com/paper/2010.01173/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning](https://mlsanity.com/paper/2404.17978/) (2024)
- [Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)](https://mlsanity.com/paper/2206.14261/) (2022)

## Abstract (arXiv)

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.