AGA3DNet improves 3D brain MRI subtype classification by feeding anatomy-guided Gaussian priors derived from radiology reports into a 3D CNN and multi-view xLSTM architecture.
arXiv preprint arXiv:2402.03526 (2024)
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Dual-head training on hierarchical OA labels yields backbone-dependent gains in KL metrics, more ordered latent severity axes, and better saliency alignment with cartilage for some 3D backbones.
citing papers explorer
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AGA3DNet: Anatomy-Guided Gaussian Priors with Multi-view xLSTM for 3D Brain MRI Subtype Classification
AGA3DNet improves 3D brain MRI subtype classification by feeding anatomy-guided Gaussian priors derived from radiology reports into a 3D CNN and multi-view xLSTM architecture.
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Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels
Dual-head training on hierarchical OA labels yields backbone-dependent gains in KL metrics, more ordered latent severity axes, and better saliency alignment with cartilage for some 3D backbones.