ProSeNet learns a sparse set of prototypes for case-based explanations in deep sequence models, matches state-of-the-art accuracy on several tasks, and supports manual prototype refinement by non-experts.
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Proposes an AI-augmented interactive system with built-in model interpretability to assist RECIST-based assessment of liver metastases.
citing papers explorer
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Interpretable and Steerable Sequence Learning via Prototypes
ProSeNet learns a sparse set of prototypes for case-based explanations in deep sequence models, matches state-of-the-art accuracy on several tasks, and supports manual prototype refinement by non-experts.
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An AI-Augmented Lesion Detection Framework For Liver Metastases With Model Interpretability
Proposes an AI-augmented interactive system with built-in model interpretability to assist RECIST-based assessment of liver metastases.