RetCCL (This paper is under review)

A better and stronger pre-trained model was built for various histopathological image applications This model outperforms ImageNet pre-trained features by a large margin. We release our best model and invite researchers to test it on your computational pathology tasks.

Hardware

  • 128GB of RAM
  • 32*Nvidia V100 32G GPUs

Preparations

1.Download all TCGA 32000 WSIs.

2.Download all PAIP 2,457 WSIs. So, there will be about 15,000,000 images(~100T). It costs us $400,000 to advance the progress of digital pathology.

Pre-trained models for histopathological image tasks

This pre-train model is here

1.Classification through search

It is the most obvious and direct way to evaluate the distinctive power of the provided features.

TissueNet
[email protected] [email protected] [email protected] [email protected]
ImageNet 50.35 77.65 87.68 46.15
CCL (ours) 67.09 87.81 93.4 70.1
UniToPatho
[email protected] [email protected] [email protected] [email protected]
ImageNet 58.17 82.89 89.45 59.01
CCL (ours) 66.55 84.32 90.31 68.35

2.Multiple Instance Learning for Whole Slide Image Classification

This task is currently based on ImageNet pretrained features, which can also verify the superiority of our feature extractor.

TCGA-NSCLC
Accuracy AUC
ABMIL 0.7719 0.8656
MIL-RNN 0.8619 0.9107
DSMIL 0.8058 0.8925
TransMIL 0.8835 0.9603
CLAM 0.8422 0.9377
CLAM+CCL (ours) 0.911 0.967

3.Classification based on features using SVM

This task follows KimiaNet

Colorectal cancer dataset
Accuracy
Combined features 87.40
Fine-tuned VGG-19 86.19
Ensemble of CNNs 92.83
KamiaNet 96.80
CCL (ours) 98.40

If you want to compute the features.

python get_feature.py

If you want to fine-tune model.

python resnet_lincls.py

Citation

Please use below to cite this paper if you find our work useful in your research.

GitHub

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