Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909207848026112 |
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| author | Talasila, Abhiroop Maity, Maitreya Priyakumar, U. Deva |
| author_facet | Talasila, Abhiroop Maity, Maitreya Priyakumar, U. Deva |
| contents | Unsupervised pre-training has emerged as a transformative paradigm, displaying remarkable advancements in various domains. However, the susceptibility to domain shift, where pre-training data distribution differs from fine-tuning, poses a significant obstacle. To address this, we augment the Swin Transformer to learn from different medical imaging modalities, enhancing downstream performance. Our model, dubbed SwinFUSE (Swin Multi-Modal Fusion for UnSupervised Enhancement), offers three key advantages: (i) it learns from both Computed Tomography (CT) and Magnetic Resonance Images (MRI) during pre-training, resulting in complementary feature representations; (ii) a domain-invariance module (DIM) that effectively highlights salient input regions, enhancing adaptability; (iii) exhibits remarkable generalizability, surpassing the confines of tasks it was initially pre-trained on. Our experiments on two publicly available 3D segmentation datasets show a modest 1-2% performance trade-off compared to single-modality models, yet significant out-performance of up to 27% on out-of-distribution modality. This substantial improvement underscores our proposed approach's practical relevance and real-world applicability. Code is available at: https://github.com/devalab/SwinFUSE |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12781 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers Talasila, Abhiroop Maity, Maitreya Priyakumar, U. Deva Computer Vision and Pattern Recognition Machine Learning Unsupervised pre-training has emerged as a transformative paradigm, displaying remarkable advancements in various domains. However, the susceptibility to domain shift, where pre-training data distribution differs from fine-tuning, poses a significant obstacle. To address this, we augment the Swin Transformer to learn from different medical imaging modalities, enhancing downstream performance. Our model, dubbed SwinFUSE (Swin Multi-Modal Fusion for UnSupervised Enhancement), offers three key advantages: (i) it learns from both Computed Tomography (CT) and Magnetic Resonance Images (MRI) during pre-training, resulting in complementary feature representations; (ii) a domain-invariance module (DIM) that effectively highlights salient input regions, enhancing adaptability; (iii) exhibits remarkable generalizability, surpassing the confines of tasks it was initially pre-trained on. Our experiments on two publicly available 3D segmentation datasets show a modest 1-2% performance trade-off compared to single-modality models, yet significant out-performance of up to 27% on out-of-distribution modality. This substantial improvement underscores our proposed approach's practical relevance and real-world applicability. Code is available at: https://github.com/devalab/SwinFUSE |
| title | Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.12781 |