Stronger is not better: Better Augmentations in Contrastive Learning for Medical Image Segmentation
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866914190683275264 |
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| author | Idris, Azeez Mohammed, Abdurahman Ali Fanijo, Samuel |
| author_facet | Idris, Azeez Mohammed, Abdurahman Ali Fanijo, Samuel |
| contents | Self-supervised contrastive learning is among the recent representation learning methods that have shown performance gains in several downstream tasks including semantic segmentation. This paper evaluates strong data augmentation, one of the most important components for self-supervised contrastive learning's improved performance. Strong data augmentation involves applying the composition of multiple augmentation techniques on images. Surprisingly, we find that the existing data augmentations do not always improve performance for semantic segmentation for medical images. We experiment with other augmentations that provide improved performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stronger is not better: Better Augmentations in Contrastive Learning for Medical Image Segmentation Idris, Azeez Mohammed, Abdurahman Ali Fanijo, Samuel Image and Video Processing Computer Vision and Pattern Recognition Self-supervised contrastive learning is among the recent representation learning methods that have shown performance gains in several downstream tasks including semantic segmentation. This paper evaluates strong data augmentation, one of the most important components for self-supervised contrastive learning's improved performance. Strong data augmentation involves applying the composition of multiple augmentation techniques on images. Surprisingly, we find that the existing data augmentations do not always improve performance for semantic segmentation for medical images. We experiment with other augmentations that provide improved performance. |
| title | Stronger is not better: Better Augmentations in Contrastive Learning for Medical Image Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.05992 |