  Tissue typology annotation in Whole Slide histological images is a complex
and tedious, yet necessary task for the development of computational pathology
models. We propose to address this problem by applying Open Set Recognition
techniques to the task of jointly classifying tissue that belongs to a set of
annotated classes, e.g. clinically relevant tissue categories, while rejecting
in test time Open Set samples, i.e. images that belong to categories not
present in the training set. To this end, we introduce a new approach for Open
Set histopathological image recognition based on training a model to accurately
identify image categories and simultaneously predict which data augmentation
transform has been applied. In test time, we measure model confidence in
predicting this transform, which we expect to be lower for images in the Open
Set. We carry out comprehensive experiments in the context of colorectal cancer
assessment from histological images, which provide evidence on the strengths of
our approach to automatically identify samples from unknown categories. Code is
released at https://github.com/agaldran/t3po .
