TOMAAT: volumetric medical image analysis as a cloud service

Fausto Milletari, Johann Frei, Seyed-Ahmad Ahmadi

Apr 11, 2018 MIDL 2018 Abstract Submission readers: everyone
  • Abstract: 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.
  • Author affiliation: nvidia, tum, lmu
  • Keywords: deep learning, medical image analysis, ultrasound, ct, mri, machine learning, cloud services, inference
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