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Recent advances in machine learning for medical imaging have led toimpressive increases in model complexity and overall capabilities. However, theability to discern the precise information a machine learning method is usingto make decisions has lagged behind and it is often unclear how theseperformances are in fact achieved. Conventional evaluation metrics that reducemethod performance to a single number or a curve only provide limited insights.Yet, systems used in clinical practice demand thorough validation that suchcrude characterizations miss. To this end, we present a framework to evaluateclassification methods based on a number of interpretable concepts that arecrucial for a clinical task. Our approach is inspired by the Turing Testconcept and how to devise a test that adaptively questions a method for itsability to interpret medical images. To do this, we make use of a TwentyQuestions paradigm whereby we use a probabilistic model to characterize themethod's capacity to grasp task-specific concepts, and we introduce a strategyto sequentially query the method according to its previous answers. The resultsshow that the probabilistic model is able to expose both the dataset's and themethod's biases, and can be used to reduced the number of queries needed forconfident performance evaluation.

Concept-Centric Visual Turing Tests for Method Validation