Open Set Medical Diagnosis

Understand

Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e.

• that models will only encounter conditions on which they have been trained.
• However, it is practically infeasible to obtain sufficient training data for every human condition, and once deployed such models will invariably face previously unseen conditions.
• We frame machine-learned diagnosis as an open-set learning problem, and study how state-of-the-art approaches compare.