  To train a robust deep learning model, one usually needs a balanced set of
categories in the training data. The data acquired in a medical domain,
however, frequently contains an abundance of healthy patients, versus a small
variety of positive, abnormal cases. Moreover, the annotation of a positive
sample requires time consuming input from medical domain experts. This scenario
would suggest a promise for one-class classification type approaches. In this
work we propose a general one-class classification model for histology, that is
meta-trained on multiple histology datasets simultaneously, and can be applied
to new tasks without expensive re-training. This model could be easily used by
pathology domain experts, and potentially be used for screening purposes.
