[MICCAI'22 Early Accept] Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection
by Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W Verjans, Gustavo Carneiro.
Please download the dataset through this link.
After downloading the dataset and extracting the I3D features using this repo, simply run the following command:
python main_transformer.pyFor inference, after setting the path of the best checkpoint, then run the following command:
python inference.pyIf you find this repo useful for your research, please consider citing our paper:
@inproceedings{tian2022contrastive,
title={Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection},
author={Tian, Yu and Pang, Guansong and Liu, Fengbei and Liu, Yuyuan and Wang, Chong and Chen, Yuanhong and Verjans, Johan and Carneiro, Gustavo},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={88--98},
year={2022},
organization={Springer}
}If you use the dataset, please also consider citing the papers below:
@inproceedings{ma2021ldpolypvideo,
title={Ldpolypvideo benchmark: A large-scale colonoscopy video dataset of diverse polyps},
author={Ma, Yiting and Chen, Xuejin and Cheng, Kai and Li, Yang and Sun, Bin},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={387--396},
year={2021},
organization={Springer}
}@article{borgli2020hyperkvasir,
title={HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
author={Borgli, Hanna and Thambawita, Vajira and Smedsrud, Pia H and Hicks, Steven and Jha, Debesh and Eskeland, Sigrun L and Randel, Kristin Ranheim and Pogorelov, Konstantin and Lux, Mathias and Nguyen, Duc Tien Dang and others},
journal={Scientific data},
volume={7},
number={1},
pages={1--14},
year={2020},
publisher={Nature Publishing Group}
}