Semi-Supervised Diffusion Model for Brain Age Prediction
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909106389909504 |
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| author | Ijishakin, Ayodeji Martin, Sophie Townend, Florence Agosta, Federica Spinelli, Edoardo Gioele Basaia, Silvia Schito, Paride Falzone, Yuri Filippi, Massimo Cole, James Malaspina, Andrea |
| author_facet | Ijishakin, Ayodeji Martin, Sophie Townend, Florence Agosta, Federica Spinelli, Edoardo Gioele Basaia, Silvia Schito, Paride Falzone, Yuri Filippi, Massimo Cole, James Malaspina, Andrea |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_09137 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semi-Supervised Diffusion Model for Brain Age Prediction Ijishakin, Ayodeji Martin, Sophie Townend, Florence Agosta, Federica Spinelli, Edoardo Gioele Basaia, Silvia Schito, Paride Falzone, Yuri Filippi, Massimo Cole, James Malaspina, Andrea Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Semi-Supervised Diffusion Model for Brain Age Prediction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.09137 |