SIMPLER learns biologically grounded SIM representations by progressively aligning them with H&E images through multiple self-supervised objectives, outperforming scratch-trained or H&E-only models on downstream tasks like multiple instance learning and clustering.
arXiv preprint arXiv:2309.07778 (2023)
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MorphDistill creates a CRC-specific encoder by distilling inter-sample relationships from multiple pathology foundation models, achieving AUC 0.68 and C-index 0.661 on stage III CRC cohorts with an 8% relative gain over baselines.
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SIMPLER: H&E-Informed Representation Learning for Structured Illumination Microscopy
SIMPLER learns biologically grounded SIM representations by progressively aligning them with H&E images through multiple self-supervised objectives, outperforming scratch-trained or H&E-only models on downstream tasks like multiple instance learning and clustering.
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MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction
MorphDistill creates a CRC-specific encoder by distilling inter-sample relationships from multiple pathology foundation models, achieving AUC 0.68 and C-index 0.661 on stage III CRC cohorts with an 8% relative gain over baselines.