SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909374204608512 |
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| author | Manoochehri, Hamid Zhang, Bodong Knudsen, Beatrice S. Tasdizen, Tolga |
| author_facet | Manoochehri, Hamid Zhang, Bodong Knudsen, Beatrice S. Tasdizen, Tolga |
| contents | Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learning, as it generates variations in image samples. However, traditional image augmentation techniques often overlook the unique characteristics of histopathological images. In this paper, we propose a new histopathology-specific image augmentation method called stain reconstruction augmentation (SRA). We integrate our SRA with MoCo v3, a leading model in self-supervised contrastive learning, along with our additional contrastive loss terms, and call the new model SRA-MoCo v3. We demonstrate that our SRA-MoCo v3 always outperforms the standard MoCo v3 across various downstream tasks and achieves comparable or superior performance to other foundation models pre-trained on significantly larger histopathology datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17514 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images Manoochehri, Hamid Zhang, Bodong Knudsen, Beatrice S. Tasdizen, Tolga Computer Vision and Pattern Recognition Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learning, as it generates variations in image samples. However, traditional image augmentation techniques often overlook the unique characteristics of histopathological images. In this paper, we propose a new histopathology-specific image augmentation method called stain reconstruction augmentation (SRA). We integrate our SRA with MoCo v3, a leading model in self-supervised contrastive learning, along with our additional contrastive loss terms, and call the new model SRA-MoCo v3. We demonstrate that our SRA-MoCo v3 always outperforms the standard MoCo v3 across various downstream tasks and achieves comparable or superior performance to other foundation models pre-trained on significantly larger histopathology datasets. |
| title | SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.17514 |