- Python 2.7 or Python 3.3+
- Pytorch 1.2.0
- sklearn
- albumentations
- cv2
- Step 1: predict the segmentation mask with the trained DeepLab model. Please check the notebook file test_segmentation_Deep_lab.ipynb (Change all the PATH director before runing the notebook.)
- Step 2: predict the final drusen mask based on the results of step 1. Please check the notebook file test_main_model.ipynb (Change all the PATH director before runing the notebook.)
Note: the pre-trained models are provided in this link.
- Step 1: train the DeepLab model. Please check the notebook file train_segmentation_Deep_lab.ipynb (Change all the PATH director before runing the notebook.)
- Step 2: predict the segmentation mask with the trained DeepLab model from step 1. Please check the notebook file test_segmentation_Deep_lab.ipynb (Change all the PATH director before runing the notebook.)
- Step 3: Crop images, ground truth drusen mask and prediction drusen mask from trained DeepLab model (step 2). Please check the notebook file patch_extract.ipynb (Change all the PATH director before runing the notebook.)
- Step 4: train the main model. Please check the notebook file train_main_model.ipynb (Change all the PATH director before runing the notebook.)