README.md

Pretrain_Chest_X-rays

Pre-training for localised tasks on Medical Chest-rays

About

Self-supervised learning has emerged as a powerful technique to understand non-curated datasets (i.e., without labels). In particular, contrastive learning has shown tremendous transfer learning capability in several downstream tasks such as Classification, segmentation and detection. In addition, we exploit the pre-training downstream. In this report, we evaluate three different scenarios involving pre-training and transfer learning to improve the localisation on chest X-rays in terms of bounding boxes.

Architecture

Stage-1 : Pre-training

Stage-2 : Downstream Training and Evaluation

Setup

1. Enviroment

Use the conda packages

conda env create -f gr2.yaml 

2. Datasets

MIMIC-CXR

• Download the dataset MIMIC-CXR from https://physionet.org/content/mimic-cxr-jpg/2.0.0/ into your folder <path-to-datasets>/MIMIC-CXR

RSNA-PNEUMONIA

• Download the dataset RSNA from this kaggle repository https://www.kaggle.com/competitions/rsna-pneumonia-detection-challenge/data into your folder <path-to-datasets>/RSNA

• Pre-process the dataset

  python src/data/datasets/mimic_cxr/mimic_cxr_dataset.py create_image_list --path <path_to_datasets>/MIMIC-CXR/mimic-cxr_ap-pa_dataset 
  

3. Stage-1 (Pre-training)

Backbone - 1: ImageNet

• For Backbone - 1 we do not need to pre-train our ResNet architecture, since we are using ResNet pre-trained on ImageNet weights. Skip to Stage-2 training.

Backbone -2: Cross Entropy

• Run the following script in the terminal

  python main_ce.py 
  --batch_size=64 
  --epochs=100 
  --learning_rate=5e-4
  --size=224
  

Backbone -3: Supervised Contrastive

• Run the following script in the terminal

  python main_supcon.py 
  --batch_size=64 
  --epochs=100 
  --learning_rate=3e-5 
  

4. Stage-2 (Downstream training and Evalaution)

• Run the following script in the terminal

  python RSNA_frozen.py 
  --batch_size=64 
  --epochs=100 
  --learning_rate=1e-3
  --backbone="ImageNet"
  --train_folder=<path_to_datasets>/RSNA/CSV/train.csv
  --val_folder=<path_to_datasets>/RSNA/CSV/validation.csv
  

  Additional Hyperparameters: If you wish to use

• Validation on Only Bounding Boxes

  --val_folder=<path_to_datasets>/RSNA/CSV/dummy.csv
  

• Backbone trained using Cross Entropy

  First, pick the best model from visualizing the loss functions in tensorboard.

  Try different learning rates, batch sizes, and choose the best epoch. Usually the last epoch works best with lowest validation loss Go to ~/save/SupCon/mimic_cxr_model/<path-to-your-best-checkpoint>

  Next, add the following command to the script (example)

   --checkpoint="~/save/SupCon/mimic_cxr_model/SupCE_mimic_cxr_resnet50_lr_0.0005_decay_1e-06_bsz_64_trial_0/last.pth"
   --backbone="SupCE"
  

• Backbone trained using Supervised Contrastive loss

  Go to ~/save/SupCon/mimic_cxr_model/<path-to-your-best-checkpoint>

  --checkpoint="~/save/SupCon/mimic_cxr_model/SupCon_mimic_cxr_resnet50_lr_3e-05_decay_1e-06_bsz_64_temp_0.07_trial_0/last.pth"
  --backbone="SupCon"
  

Configurations

Stage-1 (Pre_training) : We have used trained ResNet-50 on 20% training data of MIMIC_CXR

Stage-2 (Downstream Traning and Evaluation):

RSNA_Training_Set = 10%	RSNA_Training_Set = 50%
ImageNet + Validation = 20%	ImageNet + Validation = 20%
ImageNet + Validation = Only Boxes	ImageNet + Validation = Only Boxes
SupCon + Validation = 20%	SupCon + Validation = 20%
SupCon + Validation = Only Boxes	SupCon + Validation = Only Boxes
Cross_Entropy + Validation = 20%	Cross_Entropy + Validation = 20%
Cross_Entropy + Validation = Only_Boxes	Cross_Entropy + Validation = Only_Boxes

Results