Semi-Supervised Diffusion Model for Brain Age Prediction
This work addresses brain age prediction for neurodegenerative disease analysis, showing competitive results but is incremental as it applies an existing method to a specific domain.
The paper tackled brain age prediction on low-quality MRI data by using a semi-supervised diffusion model, achieving a 0.83 correlation with chronological age and a 0.24 correlation with survival length in ALS.
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.