  Deep neural networks have introduced significant advancements in the field of
machine learning-based analysis of digital pathology images including prostate
tissue images. With the help of transfer learning, classification and
segmentation performance of neural network models have been further increased.
However, due to the absence of large, extensively annotated, publicly available
prostate histopathology datasets, several previous studies employ datasets from
well-studied computer vision tasks such as ImageNet dataset. In this work, we
propose a transfer learning scheme from breast histopathology images to improve
prostate cancer detection performance. We validate our approach on annotated
prostate whole slide images by using a publicly available breast histopathology
dataset as pre-training. We show that the proposed cross-cancer approach
outperforms transfer learning from ImageNet dataset.
