CLEAR-HPV restructures the latent space of attention-based MIL models to discover 10 label-free morphologic concepts that preserve slide-level HPV prediction performance and generalize across TCGA-HNSCC, TCGA-CESC, and CPTAC-HNSCC datasets.
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2 Pith papers cite this work, alongside 39 external citations. Polarity classification is still indexing.
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2026 2representative citing papers
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.
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CLEAR-HPV: Interpretable concept discovery for human-papillomavirus-associated morphology in whole-slide histology
CLEAR-HPV restructures the latent space of attention-based MIL models to discover 10 label-free morphologic concepts that preserve slide-level HPV prediction performance and generalize across TCGA-HNSCC, TCGA-CESC, and CPTAC-HNSCC datasets.
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Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.