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

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

• 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.

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

• Official Website: https://monuseg.grand-challenge.org/Data/
• Baidu Disk: https://pan.baidu.com/s/1tqDzX52v8GYWXF4YfUGu1Q password: dqsr
• Google Drive: https://drive.google.com/file/d/1j7vEoq6YCBNKMoOZKPSQNciHZkVzkxGD/view?usp=sharing

TNBC

• Official Repo: https://github.com/PeterJackNaylor/DRFNS
• Baidu Disk: https://pan.baidu.com/s/1zPWTYAEffX55c2eyb3cU0Q password: zsl1
• Google Drive: https://drive.google.com/file/d/1RYY7vE0LAHSTQXvLx41civNRZvl-2hnJ/view?usp=sharing

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

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

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}
}