Semi-Supervised Diffusion Model for Brain Age Prediction
Ayodeji Ijishakin Sophie Martin Florence Townend Federica Agosta Edoardo Gioele Spinelli Silvia Basaia Paride Schito Yuri Falzone Massimo Filippi James Cole Andrea Malaspina
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.