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SGCL: Spatial Guided Contrastive Learning on Whole-slide Pathological Images

This project hosts the code for implementing the SGCL algorithm for self-supervised representation learning on whole-slide pathological imaegs.

SCGL is based on our preliminary work

SSLP: Spatial Guided Self-supervised Learning on Pathological Images,
Jiajun Li*, Tiancheng Lin*, Yi Xu
In International Conference on Medical Image Computing and Computer-Assisted Intervention. (MICCAI), 2021

Highlights

  • Novel method: SGCL is a novel self-supervised learning method for whole-slide pathological images to explore the intra-invariance, inter-invariance and inner-invariance beyond the self-invariance of instance discrimination.
  • Exploring two intrinsic properties: Spatial-propagated invariance and multi-object prior behind the WSIs are leveraged for designing the pretext tasks.
  • Loss function: A new loss function is proposed and validated via a theoretical analysis.
  • SOTA performance: The proposed method achieves state-of-the-art performance on multiple pathological datasets and tasks.

workflow

Installation

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

Main results

We conduct several evalutions on Camelyon16, NCTCRC, Nucls, BCSS.

Note:

  • The metrics for VOC det and seg are AP (COCO-style) and mIoU. The results are averaged over 3 trials.
  • The training time is measured on 4 3090 Ti GPUs.
  • See our paper for more results on different benchmarks.

Usage

Training

CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29504 \
bash tools/dist_train.sh configs/selfsup/sgcl/npidsgclmcrop.py 4 \
--work_dir ./work_dirs/selfsup/npidsgclmcrop

Extracting Backbone Weights

WORK_DIR=work_dirs/selfsup/sgcl/npidsgclmcrop.py/
CHECKPOINT=${WORK_DIR}/epoch_100.pth
WEIGHT_FILE=${WORK_DIR}/extracted_sgcl_camelyon_100ep.pth

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

Transferring to Object Detection and Segmentation

Please refer to README.md for transferring to object detection and semantic segmentation.

Tips

  • After extracting the backbone weights, the model can be used to replace the original ImageNet pre-trained model as initialization for many prediction tasks.
  • If your machine has a slow data loading issue, especially for ImageNet, your are suggested to convert ImageNet to lmdb format through folder2lmdb_imagenet.py or folder2lmdb_coco.py, and use this config_imagenet or config_coco for training.

Acknowledgement

We would like to thank the OpenSelfSup, LocalAgg and DenseCL for their open-source projects, PyContrast for its detection evaluation configs .

Citations

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

@article{lin2023sgcl,
  title={SGCL: Spatial guided contrastive learning on whole-slide pathological images},
  author={Lin, Tiancheng and Yu, Zhimiao and Xu, Zengchao and Hu, Hongyu and Xu, Yi and Chen, Chang-Wen},
  journal={Medical Image Analysis},
  pages={102845},
  year={2023},
  publisher={Elsevier}
}