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Self Path: Self Supervision for Classification of Histology Images with Limited Budget of Annotation

This is PyTorch implementation of our paper link.

Sorting Dataset

For our experiment, we save all datasets in one folder, and then we created pickle file based on the image names and the budget of annotations: For example Pickle file is a dictionary similar to {"Train1":{"WSI":[wsi names],"patches":{"Normal":[patch names], "Tumour":[patch_ names]}}, "Train0.5":....}- "Train1", "Train0.5", "Train0.2" and etc. are related to budeget of annotation for 100, 50%, 20% and etc. "WSI" is for WSI names and "patches" are for the name of patches that are extracted from WSIs. Of course, you can change the dataloader according to your preferences.

Requirements

albumentations==0.3.1 numpy==1.17.0 tqdm==4.40.0 matplotlib==3.1.1 scikit_image==0.15.0 openslide_python==1.1.1 opencv_python_headless==4.1.0.25 pandas==0.25.1 config==0.5.0.post0 imutils==0.5.3 optimizers==v1.9 Pillow==8.0.1 scikit_learn==0.23.2 skimage==0.0 spams==2.6.2.5 tensorboardX==2.1 torch==1.7.1 torchvision==0.8.2

Usage

  • All directories for the input data and also hyperparameters can be set in "config.py"

    • 'budget' in config file indicates the budget of annotation according to the dictionary mentioned above.
    • tasks in config are set by defining following list/dictionaries:
    
 tasks = {'magnification': {'type': 'classification_self', 'n_classes': 4},
         'main_task': {'type': 'classification_main', 'n_classes': 2},
         'jigsaw': {'type': 'classification_self', 'n_classes': 24},
         'domain_classifier': {'type': 'classification_adapt', 'n_classes': 2},
         'hematoxylin': {'type': 'pixel_self', 'n_classes': 1},
         'flip': {'type': 'classification_self', 'n_classes': 2},
         'rot': {'type': 'classification_self', 'n_classes': 4},
         'auto': {'type': 'pixel_self', 'n_classes': 3}
         }
         
loss_weight = {'magnification': 1, 'domain_classifier': 1,
               'main_task': 1, 'jigsaw': 1, 'hematoxylin': 1,
               'flip': 1, 'rot':1, 'auto': 1}

  • Run 'main.py' to start training for designated budget of annotations.

    • You can use your wandb account for tracking experiments.