Covariance Descriptors Meet General Vision Encoders: Riemannian Deep Learning for Medical Image Classification
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911251268894720 |
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| author | Mayr, Josef Reithmeir, Anna Di Folco, Maxime Schnabel, Julia A. |
| author_facet | Mayr, Josef Reithmeir, Anna Di Folco, Maxime Schnabel, Julia A. |
| contents | Covariance descriptors capture second-order statistics of image features. They have shown strong performance in general computer vision tasks, but remain underexplored in medical imaging. We investigate their effectiveness for both conventional and learning-based medical image classification, with a particular focus on SPDNet, a classification network specifically designed for symmetric positive definite (SPD) matrices. We propose constructing covariance descriptors from features extracted by pre-trained general vision encoders (GVEs) and comparing them with handcrafted descriptors. Two GVEs - DINOv2 and MedSAM - are evaluated across eleven binary and multi-class datasets from the MedMNSIT benchmark. Our results show that covariance descriptors derived from GVE features consistently outperform those derived from handcrafted features. Moreover, SPDNet yields superior performance to state-of-the-art methods when combined with DINOv2 features. Our findings highlight the potential of combining covariance descriptors with powerful pretrained vision encoders for medical image analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Covariance Descriptors Meet General Vision Encoders: Riemannian Deep Learning for Medical Image Classification Mayr, Josef Reithmeir, Anna Di Folco, Maxime Schnabel, Julia A. Computer Vision and Pattern Recognition Covariance descriptors capture second-order statistics of image features. They have shown strong performance in general computer vision tasks, but remain underexplored in medical imaging. We investigate their effectiveness for both conventional and learning-based medical image classification, with a particular focus on SPDNet, a classification network specifically designed for symmetric positive definite (SPD) matrices. We propose constructing covariance descriptors from features extracted by pre-trained general vision encoders (GVEs) and comparing them with handcrafted descriptors. Two GVEs - DINOv2 and MedSAM - are evaluated across eleven binary and multi-class datasets from the MedMNSIT benchmark. Our results show that covariance descriptors derived from GVE features consistently outperform those derived from handcrafted features. Moreover, SPDNet yields superior performance to state-of-the-art methods when combined with DINOv2 features. Our findings highlight the potential of combining covariance descriptors with powerful pretrained vision encoders for medical image analysis. |
| title | Covariance Descriptors Meet General Vision Encoders: Riemannian Deep Learning for Medical Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.04190 |