  Digitization of histopathology slides has led to several advances, from easy
data sharing and collaborations to the development of digital diagnostic tools.
Deep learning (DL) methods for classification and detection have shown great
potential, but often require large amounts of training data that are hard to
collect, and annotate. For many cancer types, the scarceness of data creates
barriers for training DL models. One such scenario relates to detecting tumor
metastasis in lymph node tissue, where the low ratio of tumor to non-tumor
cells makes the diagnostic task hard and time-consuming. DL-based tools can
allow faster diagnosis, with potentially increased quality. Unfortunately, due
to the sparsity of tumor cells, annotating this type of data demands a high
level of effort from pathologists. Using weak annotations from slide-level
images have shown great potential, but demand access to a substantial amount of
data as well. In this study, we investigate mitigation strategies for limited
data access scenarios. Particularly, we address whether it is possible to
exploit mutual structure between tissues to develop general techniques, wherein
data from one type of cancer in a particular tissue could have diagnostic value
for other cancers in other tissues. Our case is exemplified by a DL model for
metastatic colon cancer detection in lymph nodes. Could such a model be trained
with little or even no lymph node data? As alternative data sources, we
investigate 1) tumor cells taken from the primary colon tumor tissue, and 2)
cancer data from a different organ (breast), either as is or transformed to the
target domain (colon) using Cycle-GANs. We show that the suggested approaches
make it possible to detect cancer metastasis with no or very little lymph node
data, opening up for the possibility that existing, annotated histopathology
data could generalize to other domains.
