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[MICCAI' 22] Intervention & Interaction Federated Abnormality Detection with Noisy Clients

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FedInI

[MICCAI' 22] Intervention & Interaction Federated Abnormality Detection with Noisy Clients

By Xinyu Liu

Installation

Check FCOS for installation instructions.

Data preparation

Step 1: Download the GLRC dataset as well as the box annotations from this URL, split the GLRC subset and convert to VOC format. Use the provided client split to replace ImageSets dir.

[DATASET_PATH]
└─ GLRC
   └─ Annotations
   └─ ImageSets
   └─ JPEGImages

Step 2: Generating class-conditioned noisy annotations with this script or instance-dependant noise with this script

Step 3: change the data root for your dataset at paths_catalog.py.

Get start

Train with FedInI: (Our code currently only supports single-GPU training.)

python tools/train_net.py --config ./configs/federated/glrc.yaml SOLVER.ANNOTATIONS 0.3 OUTPUT_DIR output_fedini SOLVER.METHOD att

As a comparison, train with FedAvg:

python tools/train_net.py --config ./configs/federated/glrc.yaml SOLVER.ANNOTATIONS 0.3 OUTPUT_DIR output_fedavg SOLVER.METHOD ori

Citation

If you think this work is helpful for your project, kindly give it a star and citation:

@inproceedings{liu2022intervention,
  title={Intervention \& Interaction Federated Abnormality Detection with Noisy Clients},
  author={Liu, Xinyu and Li, Wuyang and Yuan, Yixuan},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={309--319},
  year={2022},
  organization={Springer}
}

Acknowledgements

The work is based on FCOS.

Contact

If you have any problems, please feel free to contact me at xliu423-c@my.cityu.edu.hk. Thanks.

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