2017 Winter Project Week/DeepInfer

Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|Projects List File:Deepinfer_arch.png|DeepInfer Architecture </gallery>

Key Investigators

• Alireza Mehrtash (BWH, UBC)
• Mehran Pesteie (UBC)
• Yang (Silvia) Yixin (Tianjin University)
• Tina Kapur (BWH)
• Sandy Wells (BWH)
• Purang Abolmaesumi (UBC)
• Andriy Fedorov (BWH)

Background and References

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. However, utilizing deep models during image-guided therapy procedures requires integration of several software components which is often a tedious taskfor clinical researchers. Hence, there is a gap between the state-of-the-art machine learning research and itsapplication in clinical setup.

DeepInfer enables 3D Slicer to connect to a powerful processing back-end either on the local machine or a remote processing server. Utilizing a repository of pre-trained, task-specific models, DeepInfer allows clinical researchers and biomedical engineers to choose and deploy a model on new data without the need for software development or configuration.

Project Description

Objective	Approach and Plan	Progress and Next Steps
Develop the client side as a Slicer extension. Develop the server side. Train a diabetic retinopathy classifier and add the model to the DeepInfer model repository.