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Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction
High-quality data accumulation is now becoming ubiquitous in the health domain.
Clusters in Explanation Space: Inferring disease subtypes from model explanations
The distribution of instances in the explanation space of our diagnostic classifier amplifies the different reasons for belonging to the same class - resulting in a representation that is uniquely useful for discovering latent subtypes.
EMAP: Explanation by Minimal Adversarial Perturbation
Modern instance-based model-agnostic explanation methods (LIME, SHAP, L2X) are of great use in data-heavy industries for model diagnostics, and for end-user explanations.
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