DeepInfer is a deep learning deployment toolkit and a model registry for medical data analysis with advanced AI tools. DeepInfer is an open-source toolkit for deploying deep learning models that are designed specifically for medical data analysis and processing. DeepInfer allows clinical researchers and biomedical engineers to deploy pre-trained deep learning models and use task-specific deep models, without the need for further software development and configuration. DeepInfer promotes reproducible research and dissimination of knowledge aquired with data-driven tools.
Please cite DeepInfer in your publications if it helps your research:
Mehrtash A. et al. " DeepInfer: open-source deep learning deployment toolkit for image-guided therapy." SPIE 2017. Download PDF at ResearchGate
BibTex citation:
@inproceedings{mehrtash2017deepinfer,
title={DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy},
author={Mehrtash, Alireza and Pesteie, Mehran and Hetherington, Jorden and Behringer, Peter A and
Kapur, Tina and Wells III, William M and Rohling, Robert and Fedorov, Andriy and Abolmaesumi,
Purang},
booktitle={Proceedings of SPIE--the International Society for Optical Engineering},
volume={10135},
year={2017},
organization={NIH Public Access}
}
DeepInfer is supported by National Institutes of Health (NIH P41EB015898, PI: Clare M. Tempany), National Center for Image Guided Therapy, Engineering Research Council (NSERC) of Canada and the Canadian Institutes of Health Research (CIHR).
If you have questions regarding the project or potential collaborations, please feel free to contact: Alireza Mehrtash, Department of Radiology, Brigham and Women’s Hospital.