Leveraging Semi-Supervised Diffusion Models for Accurate Brain Age Prediction
Abstract
Accurate brain age prediction has become an important biomarker in neuroimaging to quantify neurodevelopmental and neurodegenerative processes. Fully supervised learning approaches for brain age estimation in the traditional setting often demand a considerable amount of labeled datasets, which may be impractical to obtain in a clinical environment—introduced a new methodology that exploits the benefits of semi-supervised diffusion models to combine labeling and unlabeled data to improve the predictive accuracy of brain age estimation. This chapter seems to overcome some of the limitations of these approaches. As suggested by the proposed framework, diffusion models&s; generation capability is used to reconstruct various structures of brain aging, and the functions of semi-supervised learning are utilized to improve the model&s;s generalization further. In addition, extensive experiments are conducted on multimodal MRI datasets and compared with cutting-edge benchmarks; our proposed models achieve superior predictive results. High accuracy indicators of predictions speak about the efficiency of the approach to differentiate between the different age groups. This methodology enables semi-supervised diffusion models to improve brain age prediction by improving prediction performance and increasing the availability of predictions without large labeled training sets, increasing the usefulness of clinical applications. This research has implications for practice regarding…
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