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.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
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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.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.