LesionPaste

This is the official pytorch implementation of the paper:

********** [link] [arxiv]

Dataset

Three publicly-accessible datasets are used in this work.

• EyeQ: Images of grades 1-4 are all considered as abnormal. All normal images in the training set are used to train LesionPaste. [link]
• IDRiD: The lesions of a single fundus image from IDRiD are used as the true anomalies for DR anomaly detection. [link]
• MosMed: CT slices containing COVID-19 lesions are considered as abnormal. [link]

Usage

LesionPaste network

A trained model and predicted results can be downloaded here.

1. Use the following method to build your dataset:

Organize your images as follows:

├── your_data_dir
    ├── train
        ├── Normal
            ├── image1.jpg
            ├── image2.jpg
            ├── ...
        ├── Abnormal
            ├── image3.jpg
            ├── image4.jpg
            ├── ...
    ├── test
        ├── Normal
            ├── image5.jpg
            ├── image6.jpg
            ├── ...
        ├── Abnormal
            ├── image7.jpg
            ├── image8.jpg
            ├── ...

Then replace the value of 'data_path' in BASIC_CONFIG in config.py with path to your_data_dir.

Recommended environment:

• python 3.8+
• pytorch 1.5.1
• torchvision 0.6.1
• tensorboard 2.2.1
• tqdm

To install the dependencies, run:

$ git clone https://github.com/Aidanvk/LesionPaste.git
$ cd LesionPaste
$ pip install -r requirements.txt

2. Update your training configurations and hyperparameters in train.py.

3. Run to train:

$ CUDA_VISIBLE_DEVICES=x python main.py