Computer Science > Computer Vision and Pattern Recognition
arXiv:2412.00110 (cs)
[Submitted on 28 Nov 2024]
Title:Demographic Predictability in 3D CT Foundation Embeddings
View a PDF of the paper titled Demographic Predictability in 3D CT Foundation Embeddings, by Guangyao Zheng and 2 other authors
View PDFHTML (experimental)Abstract:Self-supervised foundation models have recently been successfully extended to encode three-dimensional (3D) computed tomography (CT) images, with excellent performance across several downstream tasks, such as intracranial hemorrhage detection and lung cancer risk forecasting. However, as self-supervised models learn from complex data distributions, questions arise concerning whether these embeddings capture demographic information, such as age, sex, or race. Using the National Lung Screening Trial (NLST) dataset, which contains 3D CT images and demographic data, we evaluated a range of classifiers: softmax regression, linear regression, linear support vector machine, random forest, and decision tree, to predict sex, race, and age of the patients in the images. Our results indicate that the embeddings effectively encoded age and sex information, with a linear regression model achieving a root mean square error (RMSE) of 3.8 years for age prediction and a softmax regression model attaining an AUC of 0.998 for sex classification. Race prediction was less effective, with an AUC of 0.878. These findings suggest a detailed exploration into the information encoded in self-supervised learning frameworks is needed to help ensure fair, responsible, and patient privacy-protected healthcare AI.
| Comments: | submitted to Radiology Cardiothoracic Imaging |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG) |
| Cite as: | arXiv:2412.00110 [cs.CV] |
| (orarXiv:2412.00110v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2412.00110 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Demographic Predictability in 3D CT Foundation Embeddings, by Guangyao Zheng and 2 other authors
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