Cut-Paste Consistency Learning

This is the official code repository for the WACV 2023 paper "Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation".

Installation

python -m virtualenv -p 3.6 env
source env/bin/activate

pip install -r requirements.txt
python setup.py install

Downloads

Datasets

• IDRiD - Source
• CT-ICH - Source | CV folds

Pretrained weights (PyTorch)

Example model checkpoints for the lesion segmentation tasks in IDRiD are provided:

Training

List of supported datasets and learning methods:

Type python main.py <data_module> <model> --help in the console for more details.

Example of cut-paste consistency learning on IDRiD-MA:

python main.py \
    idrid-cp \
    unet-cp \
    --unlabeled_weight 0.01 \
    --mean_teacher \
    --base_ema 0.996 \
    --seed 42 \
    --num_workers 5 \
    --batch_size 5 \
    --synth_split 0.4 \
    --num_synth 300 \
    --mask_blur gaussian \
    --background_blur gaussian  \
    --img_match \
    --val_split 0.1 \
    --data_dir data/IDRiD \
    --gpus [0] \
    --max_epochs 500 \
    --check_val_every_n_epoch 1 \
    --early_stopping_patience -1 \
    --log_every_n_steps 10 \
    --learning_rate 6e-4 \
    --warmup_epochs 10 \
    --optimizer adamw \
    --weight_decay 1e-5 \
    --lr_scheduler cosine \
    --num_layers 5 \
    --features_start 64 \
    --preprocess resize \
    --size 512 \
    --inference_mode resize \
    --inference_size 512 \
    --checkpoint_monitor "val/aupr" \
    --do_train \
    --num_sanity_val_steps 0 \
    --pos_weight 6.84 \
    --default_root_dir "model/idrid-MA" \
    --task_id MA

Example of cut-paste consistency learning on CT-ICH:

python main.py \
    ich-cp \
    unet-cp \
    --unlabeled_weight 0.1 \
    --mean_teacher \
    --base_ema 0.996 \
    --seed 42 \
    --num_workers 5 \
    --batch_size 8 \
    --labeled_split 0.7 \
    --synth_split 0.4 \
    --img_match \
    --mask_blur gaussian \
    --background_blur none \
    --data_dir "data/CT-ICH/data/fold-1" \
    --default_root_dir "model/ich" \
    --gpus [0] \
    --max_epochs 50 \
    --check_val_every_n_epoch -1 \
    --early_stopping_patience -1 \
    --log_every_n_steps 10 \
    --learning_rate 3e-5 \
    --warmup_epochs 10 \
    --optimizer adamw \
    --weight_decay 1e-5 \
    --lr_scheduler cosine \
    --num_layers 5 \
    --features_start 64 \
    --input_channels 1 \
    --preprocess resize \
    --size 512 \
    --inference_mode resize \
    --inference_size 512 \
    --do_train \
    --do_test \
    --disable_aupr \
    --num_sanity_val_steps 0 \
    --pos_weight 7.08 

Evaluation

Example of evaluating a trained model on IDRiD-MA:

python main.py \
    idrid \
    unet \
    --num_workers 1 \
    --data_dir "data/IDRiD" \
    --num_layers 5 \
    --features_start 64 \
    --inference_mode resize \
    --inference_size 512 \
    --do_test \
    --aupr_in_cpu \
    --batch_size 1 \
    --gpus 1 \
    --default_root_dir "model/test/IDRiD-MA" \
    --task_id MA \
    --resume_from_checkpoint "model/IDRiD-MA/checkpoint.ckpt"

Citation

@InProceedings{Yap_2023_WACV,
    author    = {Yap, Boon Peng and Ng, Beng Koon},
    title     = {Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2023},
    pages     = {6160-6169}
}