nu_gan

This repository contains the Python implementation for our 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

Specially thanks for the open source codes shared by caogang/wgan-gp and DigitalSlideArchive/HistomicsTK

Requirements

• Pytorch
• HistomicsTK
• An Unofficial Compiled Version of HistomicsTK (Python 3.5.6, GCC 7.3.0, Ubuntu 16.04.3 LTS)

Usage

1) Download Data

• Dataset A: Google Drive
• Dataset B: Google Drive
• (original images: Google Drive)

2) Extract

The default path should be ./experiemnt/data. You can make new directory /experiment under the root, extract the data, then rename the directory name to data. You can also open nu_gan.py to change the default path.

3) Usage

Three tasks can be chosen using flags as follows.

• Unsupervised Cell-level Classification:

python nu_gan.py --task 'cell_representation'

• Unsupervised Image-level Classification:

python nu_gan.py --task 'image_classification'

• Neuclei Segmentation:

python nu_gan.py --task 'cell_segmentation'

For convenience, the parameters for training is stored in nu_gan.py, which can be changed easily.