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README.md

BiO-Net

Keras implementation of "BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture", MICCAI 2020

Paper: https://arxiv.org/abs/2007.00243

BiO-Net

Dependencies

  • Python >= 3.6
  • tensorflow-gpu >= 1.14.0
  • PIL >= 7.0.0
  • keras >= 2.1.5

NOTE: The repo has been sucessfully tested on a GeForce RTX 2080 GPU with CUDA=10.1 and driver=435.21.

Data

Please refer to the official website (or project repo) for license and terms of usage. We preprocessed the data for adaptating our data loaders. Please find the links below.

MoNuSeg

TNBC

NOTE: You can place your own dataloader under core/dataloader.py.

Usage

Train

python3 train.py --epochs 300 --iter 3 --integrate --train_data PATH_TO_TRAIN_DATA_ROOT \
				 --valid_data PATH_TO_VALID_DATA_ROOT --exp 1

Evaluate

python3 train.py --evaluate_only --valid_data PATH_TO_VALID_DATA_ROOT --exp 1

or

python3 train.py --evaluate_only --valid_data PATH_TO_VALID_DATA_ROOT --model_path PATH_TO_TRAINED_MODEL

Citation

If you find this repo useful in your work or research, please cite:

@inproceedings{xiang2020bio,
  title={BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture},
  author={Xiang, Tiange and Zhang, Chaoyi and Liu, Dongnan and Song, Yang and Huang, Heng and Cai, Weidong},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={74--84},
  year={2020},
  organization={Springer}
}

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Keras implementation of "BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture", MICCAI 2020

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