sdc17 / HeuristicDropout

[ICASSP 2022] Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models.

Home Page:https://ieeexplore.ieee.org/abstract/document/9747409

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Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models

Official implementation of Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models.

Usage

We provide an out-of-the-box implementation of the proposed Heuristic Dropout. To use the Heuristic Dropout, simply replace the original nn.Dropout in your medical image segmentation models with it.

Cite

If you find this work useful, please consider citing the corresponding paper:

@inproceedings{shi2022heuristic,
  title={Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models},
  author={Shi, Dachuan and Liu, Ruiyang and Tao, Linmi and Yuan, Chun},
  booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1101--1105},
  year={2022},
  organization={IEEE}
}

About

[ICASSP 2022] Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models.

https://ieeexplore.ieee.org/abstract/document/9747409

License:BSD 3-Clause "New" or "Revised" License


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