1. Semi-Supervised Diffusion Model for Brain Age Prediction
- Author
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Ijishakin, Ayodeji, Martin, Sophie, Townend, Florence, Agosta, Federica, Spinelli, Edoardo Gioele, Basaia, Silvia, Schito, Paride, Falzone, Yuri, Filippi, Massimo, Cole, James, and Malaspina, Andrea
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To enhance their performance, we employ a semi-supervised diffusion model, obtaining a 0.83(p<0.01) correlation between chronological and predicted age on low quality T1w MR images. This was competitive with state-of-the-art non-generative methods. Furthermore, the predictions produced by our model were significantly associated with survival length (r=0.24, p<0.05) in Amyotrophic Lateral Sclerosis. Thus, our approach demonstrates the value of diffusion-based architectures for the task of brain age prediction.
- Published
- 2024