Microsoft Research Project InnerEye releases its AI software as open-source

The Project InnerEye team at Microsoft Research Cambridge has released its InnerEye Deep Learning Toolkit as open-source software on GitHub.

Following years of development on a machine learning toolkit, the goal of the project has been to democratise AI for medical image analysis and empower developers at research institutes, hospitals, life science organisations, and healthcare providers to build their own medical imaging AI models using Microsoft Azure.

The team has been working with the University of Cambridge and Cambridge University Hospitals NHS Foundation Trust to make progress on the platform and its application in a healthcare setting.

Consultant oncologist Dr Raj Jena, said: “Starting radiotherapy promptly improves cancer survival rates and reduces anxiety in newly diagnosed patients. But before any radiotherapy can take place, the oncologist must spend a significant amount of time – maybe one or two hours per patient – making sure the radiation will be delivered to the correct part of the body without damaging any healthy tissue.”

“Using deep learning algorithms, the InnerEye technology can carry out this preparation as well as an expert clinician in just a few minutes. This means the doctor’s time is freed up, enabling them to get patients onto treatment more quickly. At Addenbrooke’s our goal is to start curative radiotherapy within 14 days, where the national target is 28 days in accordance with the National Cancer Plan. Project InnerEye helps us realise that goal.”

“The strongest testament to the success of the technology comes in the level of engagement with InnerEye from my busy clinical colleagues. For over 15 years, the promise of automated segmentation of images for radiotherapy planning has remained unfulfilled. With the InnerEye ML model we have trained on our data, we now observe consistent segmentation performance to a standard that matches our stringent clinical requirements for accuracy.”

The InnerEye Deep Learning Toolkit can be used by researchers to build and refine models and apply them in many ways, including applications yet to be thought of. Healthcare providers, companies, and partners can also use this toolkit to develop their own ML products and services.