  A critical challenge of training deep learning models in the Digital
Pathology (DP) domain is the high annotation cost by medical experts. One way
to tackle this issue is via transfer learning from the natural image domain
(NI), where the annotation cost is considerably cheaper. Cross-domain transfer
learning from NI to DP is shown to be successful via class labels. One
potential weakness of relying on class labels is the lack of spatial
information, which can be obtained from spatial labels such as full pixel-wise
segmentation labels and scribble labels. We demonstrate that scribble labels
from NI domain can boost the performance of DP models on two cancer
classification datasets (Patch Camelyon Breast Cancer and Colorectal Cancer
dataset). Furthermore, we show that models trained with scribble labels yield
the same performance boost as full pixel-wise segmentation labels despite being
significantly easier and faster to collect.
