Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918484362919936 |
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| author | Sachan, Sapna Mahto, Amulya Kumar Patil, Prashant Wagambar |
| author_facet | Sachan, Sapna Mahto, Amulya Kumar Patil, Prashant Wagambar |
| contents | The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI data and substantial inter-institutional domain shift arising from variations in scanners, imaging protocols, and contrast settings. These challenges significantly impair model generalization in real-world settings. To address this, we propose a novel orientation-aware unsupervised domain-adaptive framework for automated brain tumor classification using mixed 2D MRI slices. Initially, a CNN with large receptive field first categorizes input slices into axial, sagittal, and coronal views. For each orientation, a CNN architecture with ResNet50 backbone augmented with four fully connected layers is trained to extract discriminative features for tumor classification. To mitigate annotation scarcity and domain discrepancies, we introduce a slice-wise unsupervised domain adaptation strategy that transfers knowledge from the multi-modal such as T1, T2, and FLAIR source domain to the post-contrast T1 target domain. Feature-level alignment is enforced using maximum mean discrepancy loss, complemented by pseudo-label guided adaptation to preserve class discriminability. Extensive experiments demonstrate improved target-domain performance over prior approaches, highlighting the benefits of orientation-specific learning, multi-modal knowledge transfer, pseudo-label-guided adaptation, and unsupervised domain adaptation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03490 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI Sachan, Sapna Mahto, Amulya Kumar Patil, Prashant Wagambar Computer Vision and Pattern Recognition The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI data and substantial inter-institutional domain shift arising from variations in scanners, imaging protocols, and contrast settings. These challenges significantly impair model generalization in real-world settings. To address this, we propose a novel orientation-aware unsupervised domain-adaptive framework for automated brain tumor classification using mixed 2D MRI slices. Initially, a CNN with large receptive field first categorizes input slices into axial, sagittal, and coronal views. For each orientation, a CNN architecture with ResNet50 backbone augmented with four fully connected layers is trained to extract discriminative features for tumor classification. To mitigate annotation scarcity and domain discrepancies, we introduce a slice-wise unsupervised domain adaptation strategy that transfers knowledge from the multi-modal such as T1, T2, and FLAIR source domain to the post-contrast T1 target domain. Feature-level alignment is enforced using maximum mean discrepancy loss, complemented by pseudo-label guided adaptation to preserve class discriminability. Extensive experiments demonstrate improved target-domain performance over prior approaches, highlighting the benefits of orientation-specific learning, multi-modal knowledge transfer, pseudo-label-guided adaptation, and unsupervised domain adaptation. |
| title | Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.03490 |