  The scarcity of labeled data is a major bottleneck for developing accurate
and robust deep learning-based models for histopathology applications. The
problem is notably prominent for the task of metastasis detection in lymph
nodes, due to the tissue's low tumor-to-non-tumor ratio, resulting in labor-
and time-intensive annotation processes for the pathologists. This work
explores alternatives on how to augment the training data for colon carcinoma
metastasis detection when there is limited or no representation of the target
domain. Through an exhaustive study of cross-validated experiments with limited
training data availability, we evaluate both an inter-organ approach utilizing
already available data for other tissues, and an intra-organ approach,
utilizing the primary tumor. Both these approaches result in little to no extra
annotation effort. Our results show that these data augmentation strategies can
be an efficient way of increasing accuracy on metastasis detection, but
fore-most increase robustness.
