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.
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.
Please refer to INSTALL.md for installation and dataset preparation.
Please see GETTING_STARTED.md for the basic usage of OpenSelfSup.
| 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.
bash tools/dist_train.sh configs/NCT_pretraining/concl/r18_bs256_ep200.py 8
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} ${WEIGHT_FILE}
Please refer to detection/README.md for transferring to dense prediction tasks in GlaS and CRAG dataset.
An example overall script is provided as below:
project_root=$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
- 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.
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
This tool is developed in Tencent AI Lab.
The copyright holder for this project is Tencent AI Lab.
All rights reserved.
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