/StructuredTriplet

Primary LanguagePythonMIT LicenseMIT

Knowledge Based Representation Learning for Nucleus Instance Classification from Histopathological Images

This repository contains PyTorch code for the paper: Knowledge Based Representation Learning for Nucleus Instance Classification from Histopathological Images

In this paper, we proposed a new framework for nucleus representation learning. We propose a new four-channel structured input and design Structured Triplet based on this input. We also add two auxiliary branches: the slide attribute learning branch and the conventional self-supervision branch, to further improve the representation encoder.

Links to the checkpoints can be found in the inference description below.

Set Up Environment

conda env create -n structured_triplet
conda activate structured_triplet
pip install torch torchvision
pip install tensorboard
pip install scikit-learn
pip install opencv

Repository Structure

Below are the main directories in the repository:

  • dataset/: the data loader and augmentation pipeline
  • tolls/: warm up training script and the main training script
  • models/: model definition
  • utils/: defines the utility function

Running the Code

Training

Dataset preparation

  • Download our dataset and place it in a local accessible directory, e.g., /dataset/LarSPI
  • Download the annotated dataset to evaluate on, e.g. panNuke or CoNSeP

Usage and Options

Usage: 

python tools/byol_warmup.py --root /dataset/LarSPI
python tools/train.py --root /dataset/LarSPI

Options:

  --name        The name of the experiment.
  --byol_name   The name of the warm up process
  --lr          Setting the learning rate.  
  --batch_size  Setting the batch size.
  --max_epoch   Setting the epochs to train.
  --alpha       Setting the hyperparameter alpha
  --beta        Setting the hyperparameter beta
  --resnet      Setting the backbone of the framework

Inference

Model Weights

As part of our work, we provide the trained model at the link below:

Usage and Options

  python tools/train.py --root_eval /dataset/panNuke

Overlaid Classification Prediction

Segmentation

Results of different classification methods on histopathological patches of 40 in PanNuke. (a) Input patch (b) SimCLR. (c) Moco (d)Moco v2 (e) BYOL (f) RCCNet (g) ViT (h) BiT (i) Structured-Triplet (j) Ground truth.

Acknowledgement

Our code on the byol branch is modified from byol-pytorch.

Citation

If any part of this code is used, please give appropriate citation to our paper. 

@ARTICLE{9869632,
  author={Zhang, Wenhua and Zhang, Jun and Yang, Sen and Wang, Xiyue and Yang, Wei and Huang, Junzhou and Wang, Wenping and Han, Xiao},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Knowledge-Based Representation Learning for Nucleus Instance Classification from Histopathological Images}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMI.2022.3201981}}

Authors

License

The dataset provided here is for research purposes only. Commercial use is not allowed. The data is held under the following license: Attribution-NonCommercial-ShareAlike 4.0 International