Unverified50% confidenceFactExact time
The largest gains from SADL occur in small-lesion scenarios, validating the targeted design of the never-empty rescue decision layer.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/12/2026
First Seen
Valid until: 1/10/2027
Sources
Related Entities
Related Claims
UnverifiedThe SADL decision layer executes an adaptive process: estimating lesion volume and applying size-bucketed thresholds, pruning connected components by voxel count and peak probability, and incorporating a 'never-empty' rescue step.79% similarUnverifiedThe never-empty rescue mechanism is especially critical in small-lesion scenarios, intercepting small-lesion samples whose probability falls slightly below the threshold and would otherwise be silently discarded.70% similarUnverifiedThe five design dimensions of SADL include: a baseline loss function combining Dice and top-K losses, strong data augmentation, reweighting by lesion volume, Stochastic Weight Averaging (SWA), and the pairing of cohort-balanced sampling with a residual encoder backbone.70% similarUnverifiedCOMED proposes a rescue-harm decomposition, where selective collaboration yields a net benefit when the number of rescued errors exceeds the harm introduced by collaboration.66% similar
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