  The lack of well-annotated datasets in computational pathology (CPath)
obstructs the application of deep learning techniques for classifying medical
images. %Since pathologist time is expensive, dataset curation is intrinsically
difficult. Many CPath workflows involve transferring learned knowledge between
various image domains through transfer learning. Currently, most transfer
learning research follows a model-centric approach, tuning network parameters
to improve transfer results over few datasets. In this paper, we take a
data-centric approach to the transfer learning problem and examine the
existence of generalizable knowledge between histopathological datasets. First,
we create a standardization workflow for aggregating existing histopathological
data. We then measure inter-domain knowledge by training ResNet18 models across
multiple histopathological datasets, and cross-transferring between them to
determine the quantity and quality of innate shared knowledge. Additionally, we
use weight distillation to share knowledge between models without additional
training. We find that hard to learn, multi-class datasets benefit most from
pretraining, and a two stage learning framework incorporating a large source
domain such as ImageNet allows for better utilization of smaller datasets.
Furthermore, we find that weight distillation enables models trained on purely
histopathological features to outperform models using external natural image
data.
