GDRNet: Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains

Table of Contents

• GDRNet: Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains
  • Table of Contents
  • News
  • Introduction
  • Getting Started
    • Data Preparation
    • Train
    • Evaluation
  • Citation

News

2023.05 Our conference paper "Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains" was early accepted by MICCAI2023 with ratio of 14%.

Introduction

Diabetic Retinopathy (DR) is a common complication of diabetes and a leading cause of blindness worldwide. Early and accurate grading of its severity is crucial for disease management. Although deep learning has shown great potential for automated DR grading, its real-world deployment is still challenging due to distribution shifts among source and target domains.

To tackle these issues, we propose a novel unified framework named Generalizable Diabetic Retinopathy Grading Network (GDRNet).

Besides the GDRNet, we provide a publicly available benchmark, GDRBench, for fair evaluations. GDRBench contains eight datasets and unified pre-processing. You can follow here to get this benchmark.

Getting Started

These instructions will help you set up the project.

Data Preparation

Follow the instructions here. Your dataset should be organized as:

.
├── images
│   ├── DATASET1
│   │   ├── mild_npdr
│   │   ├── moderate_npdr
│   │   ├── nodr
│   │   ├── pdr
│   │   └── severe_npdr
│   ├── DATASET2
│   │   ├── mild_npdr
│   │   ├── moderate_npdr
│   │   ├── nodr
│   │   ├── pdr
│   │   └── severe_npdr
│   ├── DATASET3
│   │    ...
│   ...  ...
│   
├── masks
│   ├── DATASET1
│   │   ├── mild_npdr
│   │   ├── moderate_npdr
│   │   ├── nodr
│   │   ├── pdr
│   │   └── severe_npdr
│   ├── DATASET2
│   │   ├── mild_npdr
│   │   ├── moderate_npdr
│   │   ├── nodr
│   │   ├── pdr
│   │   └── severe_npdr
│   ├── DATASET3
│   │    ...
│   ...  ...
│   
└── splits
    ├── DATASET1_crossval.txt
    ├── DATASET1_train.txt
    ├── DATASET2_crossval.txt
    ├── DATASET2_train.txt
    ├── DATASET3_crossval.txt
    ├── DATASET3_train.txt
    ...

Train

We provide our GDRNet method and seven other methods for comparison. We also have two experiment settings, DG and ESDG.

Currently, we support other methods including:

• GDRNet
• ERM
• GREEN
• CABNet
• MixupNet
• MixStyleNet

To train the DG setting, you should indicate one dataset as target domain and others as source domains. PLEASE NOTE that, DDR and Eyepacs datasets will not be regarded as source domains to train. Run the following command:

python main.py --root YOUR_DATASET_ROOT_PATH
               --algorithm ALGORITHM_NAME
               --dg_mode DG
               --source-domains DATASET2 DATASET3 ...
               --target-domains DATASET1 
               --output YOUR_OUTPUT_DIR

To train in the ESDG setting, you should indicate one dataset as source domain and others as target domains. Please run the following command:

python main.py --root YOUR_DATASET_ROOT_PATH
               --algorithm ALGORITHM_NAME
               --dg_mode ESDG
               --source-domains DATASET1
               --target-domains DATASET2 DATASET3 ...
               --output YOUR_OUTPUT_DIR

We provide other controllable args in ./utils/args.py to control the training. Besides, you can also specify the hyper-parameters for training by modifying the config files in ./configs/.

Evaluation

We evaluate the results during training. You can find and analyze the tensorboard results in ./YOUR_OUTPUT_DIR/.

Citation

@inproceedings{che2023DGDR,
  title={Towards generalizable diabetic retinopathy grading in unseen domains},
  author={Che, Haoxuan and Cheng, Yuhan and Jin, Haibo and Chen, Hao},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={430--440},
  year={2023},
  organization={Springer}
}