This is a deep learning toolbox to train models on medical images (or more generally, 3D images). It integrates seamlessly with cloud computing in Azure.
On the modelling side, this toolbox supports
- Segmentation models
- Classification and regression models
- Sequence models
Classification, regression, and sequence models can be built with only images as inputs, or a combination of images and non-imaging data as input. This supports typical use cases on medical data where measurements, biomarkers, or patient characteristics are often available in addition to images.
On the user side, this toolbox focusses on enabling machine learning teams to achieve more. It is cloud-first, and relies on Azure Machine Learning Services (AzureML) for execution, bookkeeping, and visualization. Taken together, this gives:
- Traceability: AzureML keeps a full record of all experiments that were executed, including a snapshot of the code. Tags are added to the experiments automatically, that can later help filter and find old experiments.
- Transparency: All team members have access to each other's experiments and results.
- Reproducibility: Two model training runs using the same code and data will result in exactly the same metrics. All sources of randomness like multithreading are controlled for.
- Cost reduction: Using AzureML, all compute (virtual machines, VMs) is requested at the time of starting the training job, and freed up at the end. Idle VMs will not incur costs. In addition, Azure low priority nodes can be used to further reduce costs (up to 80% cheaper).
- Scale out: Large numbers of VMs can be requested easily to cope with a burst in jobs.
Despite the cloud focus, all training and model testing works just as well on local compute, which is important for model prototyping, debugging, and in cases where the cloud can't be used.
In addition, our toolbox supports:
- Cross-validation using AzureML's built-in support, where the models for individual folds are trained in parallel. This is particularly important for the long-running training jobs often seen with medical images.
- Hyperparameter tuning using Hyperdrive.
- Building ensemble models.
- Easy creation of new models via a configuration-based approach, and inheritance from an existing architecture.
Once training in AzureML is done, the models can be deployed from within AzureML or via Azure Stack Hub.
- Setting up your environment
- Setting up Azure Machine Learning
- Creating a dataset
- Building models in Azure ML
- Sample Segmentation and Classification tasks
- Debugging and monitoring models
Please cite the following references if you find the image segmentation framework useful in your research:
Augmenting Image Guided Radiotherapy Workflows with Deep Learning. JAMA Network Open (Under Review), 2020.
Oktay O, Nanavati J, Schwaighofer A, Carter D, Bristow M, Tanno R, Jena R, Barnett G, Noble D, Rimmer Y, Glocker B, O’Hara K, Bishop C, Alvarez-Valle J, and Nori A.
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI, Springer, 2016.
Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, Olaf Ronneberger
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