ICASSP 2024 Poster. Paper: A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification.
- Linux
- Python 3.8
- NVIDIA GPU + CUDA CuDNN
- anaconda virtual environment
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia conda install tqdm conda install matplotlib==3.3.4 conda install seaborn conda install scikit-learn
-covid_ct：
├─train├─X
│ │ 001non-covid19.png
│ │ 002non-covid19.png
│ ├─Y
│ │ 001covid19.png
│ │ 002covid19.png
├─valid├─X
│ │ 001non-covid19.png
│ │ 002non-covid19.png
│ ├─Y
│ │ 001covid19.png
│ │ 002covid19.png
├─test ├─X
│ │ 001non-covid19.png
│ │ 002non-covid19.png
│ ├─Y
│ │ 001covid19.png
│ │ 002covid19.png
classification_hingeloss_preaugment
python train.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 1 \
--project_name convnext_tiny --model_name convnext_tiny --gpu_ids 0,1
python test.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 1 \
--classifier checkpoints/covid/convnext_tiny/convnext_tiny_best_netC.pth \
--project_name convnext_tiny --model_name convnext_tiny --gpu_ids 0
classification_cross-entropy_preaugment
python train.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 2 \
--project_name convnext_tiny --model_name convnext_tiny --gpu_ids 0,1
python test.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 2 \
--classifier checkpoints/covid/convnext_tiny/convnext_tiny_best_netC.pth \
--project_name convnext_tiny --model_name convnext_tiny --gpu_ids 0
ACGAN
python train.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 2 \
--project_name acgan_convnext_tiny --model_name convnext_tiny --gpu_ids 0,1
python test.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 2 \
--generator checkpoints/coivd/acgan_convnext_tiny/best_netG.pth
--classifier checkpoints/covid/acgan_convnext_tiny/best_netC.pth
--project_name acgan_convnext_tiny --model_name convnext_tiny --gpu_ids 0
VACGAN
python train.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 2 \
--project_name vacgan_convnext_tiny --model_name convnext_tiny --gpu_ids 0,1
python test.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 2 \
--generator checkpoints/coivd/vacgan_convnext_tiny/best_netG.pth
--classifier checkpoints/covid/vacgan_convnext_tiny/best_netC.pth
--project_name vacgan_convnext_tiny --model_name convnext_tiny --gpu_ids 0
ParaGAN
python train.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 1 \
--lambda_fake 0.2 --lambda_vertical 0.1 --classifier ./pretrained_covid/convnext_tiny_best_netC.pth \
--project_name cyclegan_convnext_tiny --model_name convnext_tiny --gpu_ids 0,1
python test.py --dataroot icassp2024/augmented_covid --dataset_name covid --num_classes 1 \
--generator_A2B checkpoints/covid/cyclegan_convnext_tiny/best_netG_A2B.pth \
--generator_B2A checkpoints/covid/cyclegan_convnext_tiny/best_netG_A2B.pth \
--classifier checkpoints/covid/cyclegan_convnext_tiny/best_netC.pth \
--project_name cyclegan_convnext_tiny --model_name convnext_tiny --gpu_ids 0
if you want to generate the tsne figure and the heatmaps, use the followed two lines in test.py:
# test_gd(module, data_gd_loader, data_c_loader, memory_allocation, opt)
# test_heatmap(module, data_gd_loader, data_c_loader, memory_allocation, opt)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent