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}
}