DADD disentangles anatomy and disease in a latent diffusion model using a Feature Purifier, ordinal disease embeddings, and Delta Steering to synthesize controllable ulcerative colitis progression images.
Deep residual learning for image recognition
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RankOOD detects out-of-distribution samples by training a model to predict fixed class-specific ranking permutations via the Plackett-Luce loss, achieving a 4.3% FPR95 reduction on near-OOD TinyImageNet.
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Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis
DADD disentangles anatomy and disease in a latent diffusion model using a Feature Purifier, ordinal disease embeddings, and Delta Steering to synthesize controllable ulcerative colitis progression images.
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RankOOD -- Class Ranking-based Out-of-Distribution Detection
RankOOD detects out-of-distribution samples by training a model to predict fixed class-specific ranking permutations via the Plackett-Luce loss, achieving a 4.3% FPR95 reduction on near-OOD TinyImageNet.