Register here to watch the on-demand Microsoft Research Webinar to learn more about Project InnerEye’s deep learning for cancer radiotherapy research and how to use the open-source InnerEye Deep Learning toolkit.
InnerEye is a research project from Microsoft Research Cambridge that uses state of the art machine learning technology to build innovative tools for the automatic, quantitative analysis of three-dimensional medical images. The goal of Project InnerEye is to democratize AI for medical image analysis and empower researchers, hospitals, life science organizations, and healthcare providers to build medical imaging AI models using Microsoft Azure.
“…We are pursuing AI so that we can empower every person and every institution that people build with tools of AI so that they can go on to solve the most pressing problems of our society and our economy. That’s the pursuit.”
Satya Nadella, keynote at Microsoft IGNITE 2016
Project InnerEye Technology
Project InnerEye develops machine learning techniques to help augment and make clinicians productive to be able to cope with the growing demand on healthcare; help deliver precision medicine for better patient outcomes, and; understanding how we can combine medical imaging features with other types of data to change the way we do medicine today, with the goal of enabling personalized medicine.
Project InnerEye builds upon many years of research in computer vision and machine learning. It employs algorithms such as the latest Convolutional Neural Networks for the automatic, voxel-wise segmentation of medical images.
Our latest research, published in JAMA Network Open, shows how AI can augment and accelerate clinicians’ ability to perform radiotherapy planning 13 times faster. You can hear more about it from Javier Alvarez-Valle, the Project InnerEye team lead, in this video.
Read the latest news – “Doctors at Addenbrooke’s hospital in Cambridge aim to drastically cut cancer waiting times using artificial intelligence (AI) to automate lengthy radiotherapy preparations.”
Open-source InnerEye Deep Learning Toolkit
Our mission is to democratize medical imaging AI, empowering developers, researchers, and partners to accelerate the adoption of machine learning to help improve patient outcomes and to allow clinicians to focus on their patients. We are excited to make the InnerEye Deep Learning Toolkit available as open-source software on GitHub to make this ML library and technical components available to as many people and organizations as possible.
The InnerEye Deep Learning Toolkit can be used by researchers to build and refine their own models and apply it in many ways, including applications yet to be thought of. Healthcare providers, companies and partners may use this toolkit to develop their own ML products and services, including deploying them in hospitals and clinics using Azure Machine Learning and/or Azure Stack Hub (subject to testing and regulatory approval as appropriate, such as FDA clearance, CE marking, or in-house exemption controls).
Project InnerEye and Novartis are working together on medical image deep learning models for personalized delivery of therapies through the AI Exploration program.
Project InnerEye has been working closely with the University of Cambridge and Cambridge University Hospitals NHS Foundation Trust through a deep research collaboration around radiotherapy.
“This collaboration between the InnerEye team at Microsoft, and the Department of Oncology at Addenbrooke’s is an example of the type of innovation that we wish to promote within the NHS. It is a good example of what can be achieved when the Trust works in collaboration with industry and the University in order to produce cutting edge technologies with real-world applications in patient care, to benefit the UK.”
Roland Sinker, Chief Executive Officer, Cambridge University Hospitals NHS Foundation Trust
Project InnerEye is supporting two projects as part of Microsoft’s Studies in Pandemic Preparedness research program supporting COVID-19 and future pandemic response:
Project InnerEye is working with Azure Stack to make machine learning capabilities available at the edge to allow healthcare providers and ISVs to provide low latency medical image analysis and comply with data handling regulations.
Please send an email to InnerEyeInfo@microsoft.com if you would like further information on this project.
If you have any requests or issues with the InnerEye Deep Learning Toolkit, please submit an Issue via GitHub.
InnerEye wins ‘Best Use of AI’ at the HTN (Health Tech News) Awards 2020