MS-trust: a transformer model with causal-global dual attention for enhanced MRI-based multiple sclerosis and myelitis detection
Abstract The detection of Multiple Sclerosis (MS) and Myelitis from MRI brain modalities is a challenging task that requires tailored transformer-based attention mechanisms. This paper presents MS-Trust that features a causality attention block to maintain the sequential integrity of MRI data and a...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01945-2 |
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| Summary: | Abstract The detection of Multiple Sclerosis (MS) and Myelitis from MRI brain modalities is a challenging task that requires tailored transformer-based attention mechanisms. This paper presents MS-Trust that features a causality attention block to maintain the sequential integrity of MRI data and a global attention block to capture long-range dependencies and global contextual information, with squeeze-and-excitation block to recalibrate channel-wise feature responses. MS-Trust's iterative architecture emphasizes more informative features while suppressing less useful ones, to focus on key indicators of MS and Myelitis such as lesions or areas of inflammation. MS-Trust, through its unique architecture, improves generalization through CutMix and MixUp regularizations, and a compact convolutional tokenizer to enhance the classification of MS and Myelitis. We conducted ablation studies of the main components that lead to elevated performance. Two datasets with a total of 6173 images comprised axial and sagittal MRI brain modalities that were prospectively used for Myelitis detection in the context of MS. Across all metrics, the proposed MS-Trust tend to outperform the CCT, BiT, BiT-CutMix, and BiT-MixUp benchmarking models, especially in the combined modality, indicating that MS-Trust might be better suited for handling complex data from multiple sources. Our best MS detection can reach accuracies of 90–92% for the axial, sagittal, and both modalities while the benchmarking models do not generally exceed 86%. Comparative analysis for Myelitis detection shows that MS-Trust achieves an approximated accuracy of 88–90% using both modalities while the benchmarking models do not exceed 79%. Furthermore, MS-Trust outperformed no-attention MLP architectures, plus more than 15 pretrained models and vision transformers using transfer learning. Despite many significant efforts and promising outcomes in this domain, accurate classification of Myelitis and MS subtypes remains a challenging task. Further evaluation and validation are needed to assess its generalizability to different datasets and settings. |
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| ISSN: | 2199-4536 2198-6053 |