The Standard Interpretable Model (SIM) is a Lagrangian mechanics framework that derives interpretability constraints from user premises to identify or construct optimal interpretable models.
Towards automatic concept- based explanations
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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.
citing papers explorer
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The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics
The Standard Interpretable Model (SIM) is a Lagrangian mechanics framework that derives interpretability constraints from user premises to identify or construct optimal interpretable models.
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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.