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Welcome to the Frontier Fusion Developer Portal

This chapter will give an overview of what the Frontier Fusion platform is and what role the developer portal plays in this context.

Discover developer resources​

Here are a few resources for those who want to go straight into coding:

Getting Started will explain the basics of building a prototype for Frontier Fusion.

In addition there are several tutorials which go into details of creating and onboarding different types of prototypes. Start with Python based prototype and walk your way through the remaining tutorials.

Immediate access to clinical users​

The "Frontier Fusion" research environment enables researchers to easily onboard, deploy and update prototypes. It is targeted at the radiological domain and hence focuses on the processing of DICOM data.

One of the main benefits of using the "Frontier Fusion" environment is that it provides immediate access to clinical users, which already have a syngo.via or a teamplay environment (with the respective Frontier license). In other words: These clinical users can start working with the newly onboarded prototype within minutes. More specifically, users with a syngo.via environment (or other OpenApps compatible environment) can directly download and install the prototype through the "Marketplace". teamplay users can utilize the prototype by simply enabling it in their "AI-Rad Companion Research" configuration.

This gives researchers a fast-lane to clinical evaluation of algorithms and other prototypes with the necessary data routing infrastructure being taken care of.

High level overview​

One of the fundamental ideas of the platform is that prototypes will be developed once and then deployed into multiple different target environments (currently two primary environments are supported, more planned). With this approach we try to maximize the reach of a prototype into the clinical research user base. Ultimately, feedback from research users is elemental in improving the prototype and eventually turning it into a product.

In particular, we are currently supporting the following deployment options:

  • AIRC Cloud: The prototype is deployed into the "AI-Rad Companion Research" environment. It is similar to the "AI Rad Companion" product offering and runs in the Azure cloud. The connection to the users environment will be made through the teamplay platform. Further details below.
    • This environment is ideal for customers who prefer minimal effort and cost on their side and have no objections to sending data to the cloud for processing.
  • AIRC Edge: The "AI-Rad Companion Research" environment also supports an "edge" deployment option where data stays on the users premises but management is done centrally form the cloud. Furthermore, diagnostic information is sent back to the cloud (logs) for central error analysis. This variant is combining the advantages of an on-premises and a cloud deployment. However, it has higher demands for the locally running hardware.
    • This environment is ideal for customers who prefer not to send their data to the cloud for processing but still want to have the system centrally managed by SHS.
  • syngo.via OpenApps: The prototype is deployed as a so-called OpenApps on a syngo.via system (or other OpenApps capable system).
    • This environment is ideal for customers who already have a syngo.via system and want to utilize it for additional processing.

The following image shows a high level architecture overview of the "AIRC Research" environment with the cloud and the edge deployment. It also shows how data will be routed through "teamplay Receiver" and "teamplay DICOM hub":

AIRC Research Overview

Please refer to the Getting Started guide to build and onboard your first prototype!

Develop once, run multiple​

The basic principle of using the Frontier developer portal to achieve dual deployability (AIRC and OpenApps) is simple:

  • Once the developer has developed his/her prototype executable, he/she registers it within the developer portal.
  • Afterwards, from within the developer portal, the creation of platform specific deployable artifacts is triggered. Within this step the original prototyp will be wrapped and adapted to the respective target environment.
    • For "AI Rad Companion Research" this means that it will be packaged in a Docker container while for OpenApps it is packaged into a proprietary archive format ("OAF" file).
  • These deployable artifacts will then be deployed into a specific target environment. For the AIRC this can be directly triggered from the developer portal and for OpenApps, the generated OAF file can be downloaded from the developer portal to install directly on a syngo.via or any other OpenApps capable system.
  • Dual Deployment Developer Workflow

Role of the developer portal​

The developer portal is the central "self-service" tool which allows the developer to

  • deploy their prototypes to a target environment
  • maintain control over who is using the prototype
  • get access to logs and in the future also usage statistics

Advanced and future topics​

Topics in this chapter are work in progress and only mentioned to complete the picture and provide a future outlook. They are not available yet in the context of the Frontier Fusion developer portal.

Custom user interfaces​

The Frontier Fusion platform allows developers of prototypes to develop their own specialized user interfaces for reviewing and potentially modifying the prototype results. For the purpose the Frontier Fusion platform will provide a generic DICOMweb based backend API. This API can be used by the custom user interface to retrieve prototype results and store modifications. The developer portal will allow to onboard such prototypes in the future.

Linux container based prototypes​

While currently the prototype executable must be a self-contained windows executable (see Getting Started), in the future the Frontier Fusion platform will also allow to onboard Linux container based prototypes.

Data sharing​

The Frontier Fusion platform allows users of prototypes to share data with the developer. This is closing the data loop and enables model re-training. Data sharing will be enabled in a later version of the platform.

Using Azure Machine Learning Studio for retraining your models​

Frontier Fusion integrates seamlessly with Azure Machine Learning Studio. This will allow for a professional development environment for your machine learning tasks while still maintaining the connection with the developer portal for the operational tasks.