Dual-IFM delivers state-of-the-art-comparable performance on retinal fundus images with inherent local and global interpretability through evidence maps and 2D projections after training on over 800,000 images.
IEEE transactions on pattern analysis and machine intelligence 35(8), 1798–1828 (2013)
2 Pith papers cite this work. Polarity classification is still indexing.
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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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Towards Interpretable Foundation Models for Retinal Fundus Images
Dual-IFM delivers state-of-the-art-comparable performance on retinal fundus images with inherent local and global interpretability through evidence maps and 2D projections after training on over 800,000 images.
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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.