  In Digital Pathology (DP), labeled data is generally very scarce due to the
requirement that medical experts provide annotations. We address this issue by
learning transferable features from weakly labeled data, which are collected
from various parts of the body and are organized by non-medical experts. In
this paper, we show that features learned from such weakly labeled datasets are
indeed transferable and allow us to achieve highly competitive patch
classification results on the colorectal cancer (CRC) dataset [1] and the
PatchCamelyon (PCam) dataset [2] while using an order of magnitude less labeled
data.
