Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
Ben Keel (University of Leeds),* Aaron Quyn (University of Leeds), David Jayne (University of Leeds), Samuel D. Relton (University of Leeds)
The 34th British Machine Vision Conference
Abstract
Lung cancer is responsible for 21% of cancer deaths in the UK and five-year survival rates are heavily influenced by the stage the cancer was identified at. Recent studies have demonstrated the capability of AI methods for accurate and early diagnosis of lung cancer from routine scans. However, this evidence has not translated into clinical practice with one barrier being a lack of interpretable models. This study investigates the application Variational Autoencoders (VAEs), a type of generative AI model, to lung cancer lesions. Proposed models were trained on lesions extracted from 3D CT scans in the LIDC-IDRI public dataset. Latent vector representations of 2D slices produced by the VAEs were explored through clustering to justify their quality and used in an MLP classifier model for lung cancer diagnosis, the best model achieved state-of-the-art metrics of AUC 0.98 and 93.1% accuracy. Cluster analysis shows the VAE latent space separates the dataset of malignant and benign lesions based on meaningful feature components including tumour size, shape, patient and malignancy class. We also include a comparative analysis of the standard Gaussian VAE (GVAE) and the more recent Dirichlet VAE (DirVAE), which replaces the prior with a Dirichlet distribution to encourage a more explainable latent space with disentangled feature representation. Finally, we demonstrate the potential for latent space traversals corresponding to clinically meaningful feature changes.Video
Citation
@inproceedings{Keel_2023_BMVC,
author = {Ben Keel and Aaron Quyn and David Jayne and Samuel D. Relton},
title = {Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0699.pdf}
}
Copyright © 2023
The British Machine Vision Conference is organised by