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LanGuideMedSeg-MICCAI2023

Pytorch code of MICCAI 2023 Paper-Ariadne’s Thread : Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images (Early Acceptance-Top 14%)

arXiv paper: https://arxiv.org/abs/2307.03942

MICCAI 2023 Conference paper: https://link.springer.com/chapter/10.1007/978-3-031-43901-8_69

Framework

Requirements

1. Environment
   The main mandatory dependency versions are as follows:

   python=3.8  
   torch=1.12.1  
   torchvision=0.13.1  
   pytorch_lightning=1.9.0  
   torchmetrics=0.10.3  
   transformers=4.24.0  
   monai=1.0.1  
   pandas  
   einops  
   

2. (Option)Download the pretrained model of CXR-BERT and ConvNeXt

   CXR-BERT-specialized see: https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-specialized/tree/main
   ConvNeXt-tiny see: https://huggingface.co/facebook/convnext-tiny-224/tree/main

   Download the file 'pytorch_model.bin' to './lib/BiomedVLP-CXR-BERT-specialized/' and './lib/convnext-tiny-224'
   If you want to use local model, just change the bert_type and vision_type in /config/training.yaml to local filefold path.

   ...
   MODEL:
     bert_type: ./lib/BiomedVLP-CXR-BERT-specialized
     vision_type: ./lib/convnext-tiny-224
   ...
   

   Or just use these models online:

   url = "microsoft/BiomedVLP-CXR-BERT-specialized"
   tokenizer = AutoTokenizer.from_pretrained(url,trust_remote_code=True)
   model = AutoModel.from_pretrained(url, trust_remote_code=True)
   

Dataset

1. QaTa-COV19 Dataset(images & segmentation mask)
   QaTa-COV19 Dataset See Kaggle: https://www.kaggle.com/datasets/aysendegerli/qatacov19-dataset

   We use QaTa-COV19-v2 in our experiments.

2. QaTa-COV19 Text Annotations(from thrid party)
   Check out the related content in LViT: https://github.com/HUANGLIZI/LViT

   Thanks to Li et al. for their contributions. If you use this dataset, please cite their work.

QuickStart

Our training is implemented based on PyTorch Lightning. Please check the relevant training settings in train.py and config.
For example: train_csv_path:./data/QaTa-COV19-v2/prompt/train.csv

To train a model, please execute:
python train.py
To evaluate a model, please excute:
python evaluate.py

Result explain

Some of you have expressed doubts about the results in Table 1, which are different from the std out on the results screen during training.
Please note: The results in Table 1 were obtained on the QaTa-COV19 test set. Please run evaluate.py to obtain the results on the test set instead of referring to the std out on the screen during training, while those results were obtained on the validation set!

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{zhong2023ariadne,
  title={Ariadne’s Thread: Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray Images},
  author={Zhong, Yi and Xu, Mengqiu and Liang, Kongming and Chen, Kaixin and Wu, Ming},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={724--733},
  year={2023},
  organization={Springer}
}