  The automatic and objective medical diagnostic model can be valuable to
achieve early cancer detection, and thus reducing the mortality rate. In this
paper, we propose a highly efficient multi-level malignant tissue detection
through the designed adversarial CAC-UNet. A patch-level model with a
pre-prediction strategy and a malignancy area guided label smoothing is adopted
to remove the negative WSIs, with which to lower the risk of false positive
detection. For the selected key patches by multi-model ensemble, an adversarial
context-aware and appearance consistency UNet (CAC-UNet) is designed to achieve
robust segmentation. In CAC-UNet, mirror designed discriminators are able to
seamlessly fuse the whole feature maps of the skillfully designed powerful
backbone network without any information loss. Besides, a mask prior is further
added to guide the accurate segmentation mask prediction through an extra
mask-domain discriminator. The proposed scheme achieves the best results in
MICCAI DigestPath2019 challenge on colonoscopy tissue segmentation and
classification task. The full implementation details and the trained models are
available at https://github.com/Raykoooo/CAC-UNet.
