  We systematically evaluate the performance of deep learning models in the
presence of diseases not labeled for or present during training. First, we
evaluate whether deep learning models trained on a subset of diseases (seen
diseases) can detect the presence of any one of a larger set of diseases. We
find that models tend to falsely classify diseases outside of the subset
(unseen diseases) as "no disease". Second, we evaluate whether models trained
on seen diseases can detect seen diseases when co-occurring with diseases
outside the subset (unseen diseases). We find that models are still able to
detect seen diseases even when co-occurring with unseen diseases. Third, we
evaluate whether feature representations learned by models may be used to
detect the presence of unseen diseases given a small labeled set of unseen
diseases. We find that the penultimate layer of the deep neural network
provides useful features for unseen disease detection. Our results can inform
the safe clinical deployment of deep learning models trained on a
non-exhaustive set of disease classes.
