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This repository implement the work of Kao, Po-Yu, et al. "Unsupervised 3-D feature learning for mild traumatic brain injury." International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer, Cham, 2016

License: MIT License

Python 99.11% Shell 0.89%
feature-extraction mild-traumatic-brain-injury 3d-features unsupervised-learning machine-learning clustering

mtop2016's Introduction

mTOP2016

This repository implements the work of Kao, Po-Yu, et al. "Unsupervised 3-D feature learning for mild traumatic brain injury." International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer, Cham, 2016

You are able to find more details of the mild Traumatic Brain Injury Outcome Prediction (mTOP) Challenge here.

  • If you think this work does benefit your research, please cite us:

@inproceedings{kao2016unsupervised, title={Unsupervised 3-D feature learning for mild traumatic brain injury}, author={Kao, Po-Yu and Rojas, Eduardo and Chen, Jefferson W and Zhang, Angela and Manjunath, BS}, booktitle={International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries}, pages={282--290}, year={2016}, organization={Springer} }

  • If you have any question about how to implement our work, you can reach my by sending an email to: [email protected]

LICENSE

MIT LICENSE.

Author

pykao, August 2016

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