Microsoft releases the InnerEye Deep Learning Toolkit to improve patient care

Microsoft's Project InnerEye has been involved in building and deploying machine learning models for years now. The team has been working with doctors, clinicians, oncologists, assisting them in tasks like radiotherapy, surgical planning, and quantitative radiology. This has reduced the burden on the people involved in the domain.

The firm says that the goal of Project InnerEye is to "democratize AI for medical image analysis" by allowing researchers and medical practitioners to build their own medical imaging models. With this in mind, the team released the InnerEye Deep Learning Toolkit as open-source software today. Built on top of PyTorch and integrated heavily with Microsoft Azure, the toolkit is meant to ease the process of training and deploying models.

Specifically, the InnerEye Deep Learning Toolkit will allow users to build their own image classification, segmentation, or sequential models. They will have the option to construct their own neural networks or import them from elsewhere. One of the motivations behind this project was to provide an abstraction layer for users so that they can deploy machine learning models without worrying too much about the details. As expected, the usual advantages of Azure Machine Learning Services will be bundled with the toolkit as well:

  • Scaling clusters to many compute nodes, to train models on multiple GPUs
  • Only paying per experiment
  • Saving costs by leveraging low priority nodes
  • Using the latest GPUs, Intelligent Processing Units (IPUs), and Field Programmable Gate Arrays (FPGAs)
  • Using advanced capabilities such as Azure Confidential Computing

The Project InnerEye team at Microsoft Research hopes that this toolkit will integrate machine learning technologies to treatment pathways, leading to long-term practical solutions. If you are interested in checking out the toolkit or want to contribute to it, you may check out the repository on GitHub. The full set of features offered under the toolkit can be found here.

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