  We consider annotation efficient learning in Digital Pathology (DP), where
expert annotations are expensive and thus scarce. We explore the impact of
image and input resolution on DP patch classification performance. We use two
cancer patch classification datasets PCam and CRC, to validate the results of
our study. Our experiments show that patch classification performance can be
improved by manipulating both the image and input resolution in
annotation-scarce and annotation-rich environments. We show a positive
correlation between the image and input resolution and the patch classification
accuracy on both datasets. By exploiting the image and input resolution, our
final model trained on < 1% of data performs equally well compared to the model
trained on 100% of data in the original image resolution on the PCam dataset.
