Semi-Supervised Diffusion Model for Brain Age Prediction
<|reference_start|>Semi-Supervised Diffusion Model for Brain Age Prediction: 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.<|reference_end|>
@article{ijishakin2024semi-supervised,
    title={Semi-Supervised Diffusion Model for Brain Age Prediction},
    author={Ayodeji Ijishakin, Sophie Martin, Florence Townend, Federica Agosta,
  Edoardo Gioele Spinelli, Silvia Basaia, Paride Schito, Yuri Falzone, Massimo
  Filippi, James Cole, Andrea Malaspina},
    journal={Deep Generative Models for Health Workshop, NeurIPS 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09137},
    primaryClass={eess.IV cs.CV}
}