yatingpan/chestimagenomechangedetection

This is the code repository for the ChestI maGenome Clinical Application Task 1: Change between sequential CXR exams

ChestImaGenomeChangeDetection

This is the code repository for the Chest ImaGenome Clinical Application Task 1: Change between sequential CXR exams, as presented in our NeurIPS 2021 Datasets and Benchmarks Track paper Chest ImaGenome Dataset for Clinical Reasoning.

The encoder is a torchXRayVision pre-trained ResNet101 autoencoder that is trained on several medical imaging datasets. The encoder image representations are concatenated and passed through a dense layer and a final classification layer. To train a model, download the ChestImaGenome dataset from PhysioNet. Training, validation and test splits are provided for reproducibility purposes.

If you find this code, models or results useful, please cite us using the following BibTeX:

@inproceedings{wu2021chest,
  title={Chest ImaGenome Dataset for Clinical Reasoning},
  author={Wu, Joy T and Agu, Nkechinyere Nneka and Lourentzou, Ismini and Sharma, Arjun and Paguio, Joseph Alexander and Yao, Jasper Seth and Dee, Edward Christopher and Mitchell, William G and Kashyap, Satyananda and Giovannini, Andrea and others},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
  year={2021}
}

Package Dependencies

• tables
• torch
• torchvision
• torchmetrics
• scikit-learn
• torchxrayvision
• pytorch_lightning
• ray[tune]

Dependencies can be installed with pip -r requirements.txt.