I-SECRET
This is the implementation of the MICCAI 2021 Paper "I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining".
Data preparation
- Firstly, download EyeQ dataset from EyeQ.
- Split the dataset into train/val/test according to the EyePACS challenge.
- Run
python tools/degrade_eyeq.py --degrade_dir ${DATA_PATH}$ --output_dir $OUTPUT_PATH$ --mask_dir ${MASK_PATH}$ --gt_dir ${GT_PATH}$.
Note that this scipt should be applied for usable dataset for cropping pre-processing.
- Make the architecture of the EyeQ directory as:
.
├──
├── train
│ └── crop_good
│ └── degrade_good
│ └── crop_usable
├── val
│ └── crop_good
│ └── degrade_good
│ └── crop_usable
├── test
│ └── crop_good
│ └── degrade_good
│ └── crop_usable
Here, the crop_good is the ${GT_PATH}$ in the step 3, and degrade_good is the ${OUTPUT_PATH}$ in the step 3.
Package install
Run
pip install -r requirements.txt
Run pipeline
Run the baseline model
python main.py --model i-secret --lambda_rec 1 --lambda_gan 1 --data_root_dir ${DATA_DIR}$ --gpu ${GPU_INDEXS}$ -- batch size {BATCH_SIZE}$ --name baseline --experiment_root_dir ${LOG_DIR}$
Run the model with IS-loss
python main.py --model i-secret --lambda_is 1 --lambda_gan 1 --data_root_dir ${DATA_DIR}$ --gpu ${GPU_INDEXS}$ -- batch size {BATCH_SIZE}$ --name is_loss --experiment_root_dir ${LOG_DIR}$
Run the I-SECRET model
python main.py --model i-secret --lambda_is 1 --lambda_icc 1 --lambda_gan 1 --data_root_dir ${DATA_DIR}$ --gpu ${GPU_INDEXS}$ -- batch size {BATCH_SIZE}$ --name i-secret --experiment_root_dir ${LOG_DIR}$
Visualization
Go to the ${LOG_DIR}$ / ${EXPERIMENT_NAME}$ / checkpoint, run
tensorboard --logdir ./ --port ${PORT}$
then go to localhost:${PORT}$ for detailed logging and visualization.
Test and evalutation
Run
python main.py --test --resume 0 --test_dir ${INPUT_PATH}$ --output_dir ${OUTPUT_PATH}$ --name ${EXPERIMENT_NAME}$ --gpu ${GPU_INDEXS}$ -- batch size {BATCH_SIZE}$
Please note that the metric outputted by test script is under the PyTorch pre-process (resize etc.). It is not precise. Therefore, we need to run the evaluation scipt for further evaluation.
python tools/evaluate.py --test_dir ${OUTPUT_PATH}$ --gt_dir ${GT_PATH}$
Vessel segmentation
We apply the iter-Net framework. We simply replace the test set with the degraded images/enhanced images. For more details, please follow IterNet.
Future Plan
- Cleaning codes
- More SOTA backbones (ResNest ...)
- WGAN loss
- Internal evaluations for down-sampling tasks
Acknowledgment
Thanks for CutGAN for the implementation of patch NCE loss, EyeQ_Enhancement for degradation codes, Slowfast for the distributed training codes
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