CONSIGN applies conformal prediction to segmentation by incorporating spatial structure through decomposition, producing tighter and more interpretable uncertainty estimates with error guarantees.
Armato III, Geoffrey McLennan, Luc Bidaut, Michael F
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VoxelFM learns robust 3D CT visual features via DINO self-distillation that transfer effectively to seven clinical task categories using frozen backbones and lightweight heads, outperforming prior CT foundation models even on report generation.
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CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition
CONSIGN applies conformal prediction to segmentation by incorporating spatial structure through decomposition, producing tighter and more interpretable uncertainty estimates with error guarantees.
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Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks
VoxelFM learns robust 3D CT visual features via DINO self-distillation that transfer effectively to seven clinical task categories using frozen backbones and lightweight heads, outperforming prior CT foundation models even on report generation.