CheSS: Chest X-ray pre-trained model via Self-Supervised contrastive learning

This is a PyTorch implementation of the CheSS paper:

@article{Cho2023,
  doi = {10.1007/s10278-023-00782-4},
  url = {https://doi.org/10.1007/s10278-023-00782-4},
  year = {2023},
  month = jan,
  publisher = {Springer Science and Business Media {LLC}},
  author = {Kyungjin Cho and Ki Duk Kim and Yujin Nam and Jiheon Jeong and Jeeyoung Kim and Changyong Choi and Soyoung Lee and Jun Soo Lee and Seoyeon Woo and Gil-Sun Hong and Joon Beom Seo and Namkug Kim},
  title = {{CheSS}: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning},
  journal = {Journal of Digital Imaging}
}

Requirements

pip install -r requirements.txt

Pretrained model weight

Google Drive

model = resnet50(num_classes=1000)

pretrained_model = "CheSS pretrained model path"
if pretrained_model is not None:
    if os.path.isfile(pretrained_model):
        print("=> loading checkpoint '{}'".format(pretrained_model))
        checkpoint = torch.load(pretrained_model, map_location="cpu")

        # rename moco pre-trained keys
        state_dict = checkpoint['state_dict']
        for k in list(state_dict.keys()):
            # retain only encoder_q up to before the embedding layer
            if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
                # remove prefix
                state_dict[k[len("module.encoder_q."):]] = state_dict[k]
            # delete renamed or unused k
            del state_dict[k]

        msg = model.load_state_dict(state_dict, strict=False)
        assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}

        print("=> loaded pre-trained model '{}'".format(pretrained_model))
    else:
        print("=> no checkpoint found at '{}'".format(pretrained_model))

    ##freeze all layers but the last fc
    for name, param in model.named_parameters():
        if name not in ['fc.weight', 'fc.bias']:
            param.requires_grad = False
            
    model.fc = nn.Linear(2048, num_class)

or you can use gdown in Python

!gdown https://drive.google.com/uc?id=1C_Gis2qcZcA9X3l2NEHR1oS4Gn_bTxTe

Contact

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