Low-field MRI offers accessible neuroimaging in low-resource settings, but is often degraded by diverse artifacts that compromise diagnostic utility. In this work, we address Task 1 of the LISA Challenge 2025, which involves multi-label ordinal classification of seven artifact types in 3D neonatal brain MRIs acquired at 0.064T. Our pipeline employs a quality-aware 3D-to-2D projection strategy that automatically selects the optimal viewing plane based on voxel resolution, followed by view-conditional dual-task learning that jointly predicts artifact severity and brain bounding boxes. By combining brain-focused morphological preprocessing, MaxViT-based feature extraction with view embeddings, and multi-scale probability aggregation across slices, our approach achieves implicit spatial attention without explicit 3D modeling. To handle severe class imbalance and label ambiguity, we apply dynamic focal loss with class-specific weights, stratified patient-level cross-validation, and targeted data augmentation. By aggregating predictions across all valid 2D slices (80–120 per subject), we achieve a weighted F1 score of 0.771 on the test set. We analyze per-artifact performance and demonstrate that efficient 2D view-conditional modeling can match or exceed 3D approaches while maintaining computational efficiency suitable for clinical deployment in resource-limited settings.