Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912188269068288 |
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| author | Shehata, Nairouz Piçarra, Carolina Glocker, Ben |
| author_facet | Shehata, Nairouz Piçarra, Carolina Glocker, Ben |
| contents | We investigate combining imaging and shape features extracted from MRI for the clinically relevant tasks of brain age prediction and Alzheimer's disease classification. Our proposed model fuses ResNet-extracted image embeddings with shape embeddings from a bespoke graph neural network. The shape embeddings are derived from surface meshes of 15 brain structures, capturing detailed geometric information. Combined with the appearance features from T1-weighted images, we observe improvements in the prediction performance on both tasks, with substantial gains for classification. We evaluate the model using public datasets, including CamCAN, IXI, and OASIS3, demonstrating the effectiveness of fusing imaging and shape features for brain analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression Shehata, Nairouz Piçarra, Carolina Glocker, Ben Computer Vision and Pattern Recognition Machine Learning Image and Video Processing We investigate combining imaging and shape features extracted from MRI for the clinically relevant tasks of brain age prediction and Alzheimer's disease classification. Our proposed model fuses ResNet-extracted image embeddings with shape embeddings from a bespoke graph neural network. The shape embeddings are derived from surface meshes of 15 brain structures, capturing detailed geometric information. Combined with the appearance features from T1-weighted images, we observe improvements in the prediction performance on both tasks, with substantial gains for classification. We evaluate the model using public datasets, including CamCAN, IXI, and OASIS3, demonstrating the effectiveness of fusing imaging and shape features for brain analysis. |
| title | Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2501.07994 |