SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
Lonini et al
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
Unsupervised discriminator-guided fine-tuning of a pretrained u-sleep model improves Cohen's kappa by 0.03-0.29 on artificially degraded sleep signals but falls short of supervised optima and yields insignificant gains on real domain mismatches.
A multi-channel governance framework for a deployed ambient AI scribe achieved measurable improvements in clinician-validated performance and feedback quality through continuous rubric evaluation, live monitoring, and controlled experiments.
citing papers explorer
-
VitaminP: cross-modal learning enables whole-cell segmentation from routine histology
VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.