This repository contains the implementation of our reproducibility study of the paper Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks, Bo Hu♯ , Ye Tang♯ , Eric I-Chao Chang, Yubo Fan, Maode Lai and Yan Xu* (* corresponding author; ♯ equal contribution), arxiv, IEEE

Original paper's code can be found on nu-gan

To install requirements:

conda create -n myenv python=3.8.5
conda activate myenv

conda install -c anaconda pip

# install PyTorch - https://pytorch.org/get-started/locally/
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch

pip install -r requirements.txt

To download datasets:

• Extract all data to desired path: Datasets

Dataset A is a labeled dataset, new dataset is unlabeled.

To train the model(s) in the paper, separate tasks can be chosen using flags. Make sure the datasets are extracted in the current directory: cd path_to_datasets

python /path_to_utils/nu_gan.py --task 'cell_representation'

• To change the number of clusters:

python /path_to_utils/nu_gan.py --task 'cell_representation' --dis_category 4

The default number of clusters (dis_category) is 5.

python /path_to_utils/nu_gan.py --task 'cell_representation_unlabeled'

• To change the number of clusters:

python /path_to_utils/nu_gan.py --task 'cell_representation_unlabeled' --dis_category 4

The default number of clusters (dis_category) is 5.

Make sure the current directory has the experiment folders: cd path_to_experiments.

python /path_to_utils/nu_gan.py --task 'cell_classification' --experiment_id 123456

experiment_id identifies the experiment from cell-level clustering to perform cell classification. Can be retrieved from the output folder name from cell_representation or cell_representation_unlabeled.

Other hyperparameters that don't have specified flags can be changed in nu_gan.py.

To evaluate the model on labeled data:

python /path_to_utils/eval.py --model_path "path_to_models" --dis_category 5

The default number of clusters (dis_category) is 5.

You can download pretrained models here:

• Unsupervised Cell-level Clustering on labeled dataset trained on the labeled Dataset A.
• Unsupervised Cell-level Clustering on unlabeled dataset trained on the unlabeled new dataset.

Our model achieves the following performance on :

Where V(D, G) is the value function of the discriminator and generator networks and Lq is the loss of the auxiliary network.

If you'd like to contribute, or have any suggestions, you can contact us at [email protected] or open an issue on this GitHub repository.

All contributions welcome! All content in this repository is licensed under the MIT license.