  This paper presents a solution to the cross-domain adaptation problem for 2D
surgical image segmentation, explicitly considering the privacy protection of
distributed datasets belonging to different centers. Deep learning
architectures in medical image analysis necessitate extensive training data for
better generalization. However, obtaining sufficient diagnostic and surgical
data is still challenging, mainly due to the inherent cost of data curation and
the need of experts for data annotation. Moreover, increased privacy and legal
compliance concerns can make data sharing across clinical sites or regions
difficult. Another ubiquitous challenge the medical datasets face is inevitable
domain shifts among the collected data at the different centers. To this end,
we propose a Client-server deep federated architecture for cross-domain
adaptation. A server hosts a set of immutable parameters common to both the
source and target domains. The clients consist of the respective
domain-specific parameters and make requests to the server while learning their
parameters and inferencing. We evaluate our framework in two benchmark
datasets, demonstrating applicability in computer-assisted interventions for
endoscopic polyp segmentation and diagnostic skin lesion detection and
analysis. Our extensive quantitative and qualitative experiments demonstrate
the superiority of the proposed method compared to competitive baseline and
state-of-the-art methods. Codes are available at:
https://github.com/thetna/distributed-da
