Official Pytorch Implementation for "CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification" (Early Accepted in MICCAI 2023 https://link.springer.com/chapter/10.1007/978-3-031-43987-2_47)
CellGAN synthesizes 256×256 cytopathological images of different cervical squamous cell types (NILM, ASC-US, LSIL, ASC-H, and HSIL
). It can serve as a data augmentation tool for patch-level cell classification in automatic cervical abnormality screening.
We provide a pre-trained CellGAN generator checkpoints/model.pth
for synthesizing cytopathological images.
Use the following command to synthesize a certain number of images for a desired cervical cell type.
python cellgan_inference.py --config [config_name] --model [model_path] --output_dir [directory to save generated images] --cell_type [desired cell type] --data_num [number of generated images]
Refer to configs/default_config.yaml
for customizing your own configuration file configs/{config_name}.yaml
. All the arguments are self-explanatory by their names and comments.
Set the argument DATAROOT
in configs/{config_name}.yaml
to your training data root.
In DATAROOT
, split your images into subdirectories according to the cell types and prepare an img_list.txt
.
The directory structure of DATAROOT
should be prepared as in the following example:
DATAROOT
├─ NILM
| ├─ NILM_image_0001.png
| └─ ......
├─ ASC_US
| ├─ ASC_US_image_0001.png
| └─ ......
├─ LSIL
| ├─ LSIL_image_0001.png
| └─ ......
├─ ASC_H
| ├─ ASC_H_image_0001.png
| └─ ......
├─ HSIL
| ├─ HSIL_image_0001.png
| └─ ......
└─ img_list.txt
img_list.txt
should contain one image path {category_name}/{image_name}.png
per line as in the following example.NILM/NILM_image_0001.png
NILM/NILM_image_0002.png
......
ASC_US/ASC_US_image_0001.png
......
python train.py --config [config_name]
Edit the testing arguments in configs/{config_name}.yaml
and use the following command:
python test.py --config [config_name]
Authors:
Zhenrong Shen[1], Maosong Cao[2], Sheng Wang[1,3], Lichi Zhang[1], Qian Wang[2]*
Institution: [1] School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
[2] School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
[3] Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
Manuscript Link: https://arxiv.org/abs/2307.06182 (preprint on arXiv)
https://link.springer.com/chapter/10.1007/978-3-031-43987-2_47 (MICCAI 2023, conference version)
Citation:
@inproceedings{shen2023cellgan, title={CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification}, author={Shen, Zhenrong and Cao, Maosong and Wang, Sheng and Zhang, Lichi and Wang, Qian}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, pages={487--496}, year={2023}, organization={Springer} }