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[ECCV22] ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images

This project hosts the code for the paper

ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images,
Jiawei Yang, Hanbo Chen, Yuan Liang, Junzhou Huang, Lei He and Jianhua Yao
European Conference on Computer Vision (ECCV), 2022

Please consider citing our paper in your publications if the paper or the project helps your research.

ConCL overview

Note

This project is developed upon the open-source project OpenSelfSup, which has now chanegd to MMselfsup. Therefore we provide the full repository we used. It should be straightforward to adopt this repo to new MMselfsup. If you have any issue, please leave an issue.

Installation

Please refer to INSTALL.md for installation and dataset preparation.

Getting Started

Please see GETTING_STARTED.md for the basic usage of OpenSelfSup.

Models

pre-train method	backbone	# pre-training epoch	GlaS AP_bb	CRAG AP_bb
SimCLR	ResNet-18	200	50.7	49.2
BYOL	ResNet-18	200	50.9	49.9
PCL-v2	ResNet-18	200	49.4	51.0
MoCo-v1	ResNet-18	200	50.0	47.2
MoCO-v2	ResNet-18	200	52.3	50.0
DenseCL	ResNet-18	200	53.9	52.3
ConCL	ResNet-18	200	56.8	55.4
SimCLR	ResNet-18	400	51.6	48.9
BYOL	ResNet-18	400	50.8	50.3
MoCo-v1	ResNet-18	400	50.6	46.7
MoCO-v2	ResNet-18	400	54.6	51.7
DenseCL	ResNet-18	400	55.1	52.9
ConCL	ResNet-18	400	58.2	54.7
SimCLR	ResNet-18	800	50.6	48.1
BYOL	ResNet-18	800	50.2	49.3
MoCo-v1	ResNet-18	800	49.8	47.2
MoCO-v2	ResNet-18	800	55.2	51.8
DenseCL	ResNet-18	800	56.0	52.5
ConCL	ResNet-18	800	58.6	55.0

The pre-trained models are available at this link.

Usage

Training

bash tools/dist_train.sh configs/NCT_pretraining/concl/r18_bs256_ep200.py 8

Extracting Backbone Weights

WORK_DIR=work_dirs/NCT_pretraining/concl/r18_bs256_ep200/
CHECKPOINT=${WORK_DIR}/epoch_200.pth
WEIGHT_FILE=${WORK_DIR}/concl_r18_bs256_ep200.pth

python tools/extract_backbone_weights.py ${CHECKPOINT} ${WEIGHT_FILE}

Transferring to Object Detection and Instance Segmentation

Please refer to detection/README.md for transferring to dense prediction tasks in GlaS and CRAG dataset.

An example overall script is provided as below:

project_root=$PROJECT_ROOT

cd $project_root

model=concl
config=r18_bs256_ep200
epoch=200

conda activate open-mmlab

mkdir work_dirs/pretrained_models

python tools/extract_backbone_weights.py  \
    work_dirs/NCT_pretraining/$model/$config/epoch_$epoch.pth \
    work_dirs/pretrained_models/$model/$config.pth

conda activate detectron2

cd benchmarks/detection

mkdir -p work_dirs/converted_weights

python convert-pretrain-to-detectron2.py \
    ../../work_dirs/wsi_pretrains/$model/$config.pth    \
    work_dirs/converted_weights/$model/$config.pkl

# 5 independent runs of fine-tuning.
for i in {1..5}
do 
    bash run.sh configs/GlaS/GlaS_R_18_FPN_1x_moco.yaml \
        work_dirs/converted_weights/$model/$config.pkl  \
        8  \
        work_dirs/wsi_submission/GlaS/$model/$config/GlaS_R_18_FPN_1x_moco/$i
done

# 5 independent runs of fine-tuning.
for i in {1..5}
do 
    bash run.sh configs/CRAG/CRAG_R_18_FPN_1x_moco.yaml \
        work_dirs/converted_weights/$model/$config.pkl  \
        8  \
        work_dirs/wsi_submission/GlaS/$model/$config/CRAG_R_18_FPN_1x_moco/$i
done

Acknowledgement

We would like to thank the OpenSelfSup (now MMselfsup) for its open-source project.
We would like to thank the SCRL for the implementation of views' transformation parameters reserving.

Disclaimer

This tool is for research purpose and not approved for clinical use.

This is not an official Tencent product.

Copyright

This tool is developed in Tencent AI Lab.