Semi-Supervised Diffusion Model for Brain Age Prediction
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要