  Recently, deep neural networks have demonstrated comparable and even better
performance with board-certified ophthalmologists in well-annotated datasets.
However, the diversity of retinal imaging devices poses a significant
challenge: domain shift, which leads to performance degradation when applying
the deep learning models to new testing domains. In this paper, we propose a
novel unsupervised domain adaptation framework, called Collaborative Feature
Ensembling Adaptation (CFEA), to effectively overcome this challenge. Our
proposed CFEA is an interactive paradigm which presents an exquisite of
collaborative adaptation through both adversarial learning and ensembling
weights. In particular, we simultaneously achieve domain-invariance and
maintain an exponential moving average of the historical predictions, which
achieves a better prediction for the unlabeled data, via ensembling weights
during training. Without annotating any sample from the target domain, multiple
adversarial losses in encoder and decoder layers guide the extraction of
domain-invariant features to confuse the domain classifier and meanwhile
benefit the ensembling of smoothing weights. Comprehensive experimental results
demonstrate that our CFEA model can overcome performance degradation and
outperform the state-of-the-art methods in segmenting retinal optic disc and
cup from fundus images. \textit{Code is available at
\url{https://github.com/cswin/AWC}}.
