Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI
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 ...
| Authors | Sapna Sachan, Amulya Kumar Mahto, Prashant Wagambar Patil |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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