A Transformer-based patient-specific non-rigid point cloud registration pipeline with overlap estimation and physics-based refinement outperforms generic methods on synthetic laparoscopic data.
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2026 2verdicts
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Pretrained autoencoders in medical latent diffusion encode discriminative features well for reconstruction but structure their latent spaces in ways that hinder classifier learning, a gap that persists across architectures and is not closed by domain fine-tuning.
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Towards Patient-Specific Deformable Registration in Laparoscopic Surgery
A Transformer-based patient-specific non-rigid point cloud registration pipeline with overlap estimation and physics-based refinement outperforms generic methods on synthetic laparoscopic data.
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The Learnability Gap in Medical Latent Diffusion
Pretrained autoencoders in medical latent diffusion encode discriminative features well for reconstruction but structure their latent spaces in ways that hinder classifier learning, a gap that persists across architectures and is not closed by domain fine-tuning.