  The automated segmentation of cancer tissue in histopathology images can help
clinicians to detect, diagnose, and analyze such disease. Different from other
natural images used in many convolutional networks for benchmark,
histopathology images can be extremely large, and the cancerous patterns can
reach beyond 1000 pixels. Therefore, the well-known networks in the literature
were never conceived to handle these peculiarities. In this work, we propose a
Fully Convolutional DenseUNet that is particularly designed to solve
histopathology problems. We evaluated our network in two public pathology
datasets published as challenges in the recent MICCAI 2019: binary segmentation
in colon cancer images (DigestPath2019), and multi-class segmentation in
prostate cancer images (Gleason2019), achieving similar and better results than
the winners of the challenges, respectively. Furthermore, we discussed some
good practices in the training setup to yield the best performance and the main
challenges in these histopathology datasets.
