README.md

SimChest: a novel anomaly agnostic model for similarity measurement in follow-up chest radiograph pairs via a supervised contrastive learning model

This is a PyTorch implementation of the LDH under reivew paper:

Pretraining task procedure

Model training

CUDA_VISIBLE_DEVICES=0,1,2,3 python main_supcon.py --dataset real --name SupCon_scratch \
--print_freq=5 --save_freq 1 --num_workers 8 --aug True --warm \
--batch_size 8 --model resnet50 --method SupCon --epochs 100

Model inference

If you want to get CXR image similarity logit, run the code below.

python model_inference.py

Result

Contact

Page: https://mi2rl.co

Email: kjcho.amc@gmail.com

Reference

@Article{khosla2020supervised,
    title   = {Supervised Contrastive Learning},
    author  = {Prannay Khosla and Piotr Teterwak and Chen Wang and Aaron Sarna and Yonglong Tian and Phillip Isola and Aaron Maschinot and Ce Liu and Dilip Krishnan},
    journal = {arXiv preprint arXiv:2004.11362},
    year    = {2020},
}