PCRLv2 (TPAMI'23)

This repository contains an official implementation of PCRLv2 (:heavy_exclamation_mark:fine-tuning code was also included). The accompanying paper "A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis" has been accepted by IEEE TPAMI.

Note that PCRLv2 is an improved version of PCRLv1 "Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts".

Dependencies

Please make sure your PyTorch version >=1.1 before you run the code. We strongly recommend you to install Anaconda3 where we use Python 3.6. In addition, we use apex for acceleration. We also use pretrained-models.pytorch and segmentation_models_pytorch to speed up the implementation.

How to Load Pretrained Models (2D & 3D)

First, git clone our repository:

git clone https://github.com/RL4M/PCRLv2.git
cd PCRLv2
mkdir pretrained_weight

Download the pretrained model weights from https://drive.google.com/drive/folders/1Rp7CJblA5HzX5xjc_hUEcD2kWFEiTboF?usp=sharing. Put them under the pretrained_weight/ folder:

pretrained_weight/
|--- simance_multi_crop_luna_pretask_1.0_240.pt
|--- simance_multi_crop_chest_pretask_1.0_240.pt
|--- simance_multi_crop_chexpter_pretask_1.0_240.pt

Here, simance_multi_crop_luna_pretask_1.0_240.pt, simance_multi_crop_chest_pretask_1.0_240.pt, and simance_multi_crop_chexpter_pretask_1.0_240.pt correspond to pre-trained models on LUNA, NIH ChestX-ray, and CheXpert, respectively.

Load the Encoder Part of a 2D Model

import segmentation_models_pytorch as smp
aux_params = dict(
            pooling='avg',  # one of 'avg', 'max'
            dropout=0.2,  # dropout ratio, default is None
            activation='sigmoid',  # activation function, default is None
            classes=n_class,  # define number of output labels
        )
weight_path = './pretrained_weight/simance_multi_crop_chexpter_pretask_1.0_240.pt'
model = smp.Unet('resnet18', in_channels=3, aux_params=aux_params, classes=1, encoder_weights=None)
encoder_dict = torch.load(weight_path)['state_dict']
encoder_dict['fc.bias'] = 0
encoder_dict['fc.weight'] = 0
model.encoder.load_state_dict(encoder_dict)

Load the Encoder Part of a 3D Model

from models.pcrlv2_model_3d import PCRLv23d
model = PCRLv23d()
weight_path = './pretrained_weight/simance_multi_crop_luna_pretask_1.0_240.pt'
model_dict = torch.load(weight)['state_dict']
model.load_state_dict(model_dict)

---

How to Perform Pre-training

on NIH ChestX-ray

Step 1

Download NIH ChestX-ray from here. The image folder of NIH ChestX-ray should be organized as follows:

Chest14/
|--- images/
|	|--- 00002639_006.png
|	|--- 00010571_003.png
|	|--- ...

Besides, we also provide the list of images used for pre-training: train_val_txt/chest_train.txt. Specifically, for semi-supervised experiments, we use top K% images for pre-training and last (100-K)% for fine-tuning. For instance, given a ratio of 9.5:0.5, we use the first 95% images in chest_train.txt for pre-training, while the rest 5% images are used for fine-tuning.

Step 2

For pre-training on NIH ChestX-ray, you can run the following script:

python main.py --data path_to_chest14 --model pcrlv2 --b 64 --epochs 240 --lr 1e-2 --output  saved_dir --n chest --d 2 --gpus 0,1,2,3 --ratio 1.0 --amp

or

bash run2d.sh

--data defines the path where you store NIH ChestX-ray.

--d defines the type of dataset, 2 stands for 2D while 3 denotes 3D.

--n gives the name of dataset.

--ratio determines the percentage of images in the training set used for pre-training. Specifically, 1 means using all training images in the training set to for pre-training.

--amp denotes whether you want to use apex to accelerate the training process.

Following a similar protocol, you can also perform pre-training on CheXpert.

on LUNA

Step 1

Download LUNA16 from here. The image folder of LUNA16 should be organized as follows:

LUNA16/
|--- subset0/   		   	
|	|--- 1.3.6.1.4.1.14519.5.2.1.6279.6001.979083010707182900091062408058.raw
|	|--- 1.3.6.1.4.1.14519.5.2.1.6279.6001.979083010707182900091062408058.mhd
|	|--- ...
|--- subset1/
|--- subset2/
|--- ...
|--- subset9/

We provide the list of training images in train_val_txt/luna_train.txt. Similar to NIH ChestX-ray, we used luna_train.txt for semi-supervised experiments, where top K% images in luna_train.txt are used for pre-training, and the rest are for fine-tuning.

Step 2

First, it is suggested to pre-process the LUNA dataset to get cropped pairs from 3D images.

python luna_preprocess.py --input_rows 64 --input_cols 64 --input_deps 32 --data path_to_LUNA --save path_to_processedLUNA

Then, we can start pre-training.

Step 3

python main.py --data path_to_processedLUNA  --model pcrlv2 --b 32 --epochs 240 --lr 1e-3 --output saved_dir  --n luna --d 3 --gpus 0,1,2,3 --ratio 1.0 --amp

or

bash run3d.sh

---

How to Perform Fine-tuning

Please refer to the finetune branch!