  Deep learning has been recently applied to a multitude of computer vision and
medical image analysis problems. Although recent research efforts have improved
the state of the art, most of the methods cannot be easily accessed, compared
or used by either researchers or the general public. Researchers often publish
their code and trained models on the internet, but this does not always enable
these approaches to be easily used or integrated in stand-alone applications
and existing workflows. In this paper we propose a framework which allows easy
deployment and access of deep learning methods for segmentation through a
cloud-based architecture. Our approach comprises three parts: a server, which
wraps trained deep learning models and their pre- and post-processing data
pipelines and makes them available on the cloud; a client which interfaces with
the server to obtain predictions on user data; a service registry that informs
clients about available prediction endpoints that are available in the cloud.
These three parts constitute the open-source TOMAAT framework.
