Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification

This is an official implementation of the paper,

Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification
Wongi Park, Jongbin Ryu.
(Comming soon..),

Environment Setting

• Conda environment : Ubuntu 18.04 CUDA-10.1 (10.2) with Pytorch==1.13.0, Torchvision==0.6.0 (python 3.8).
  

# Create Environment
conda create -n fgssl python=3.8
conda activate fgssl

# Install pytorch, torchvision, cudatoolkit
conda install pytorch==1.13.0 torchvision==0.6.0 cudatoolkit=10.1 (10.2) -c pytorch

Overall Framework

Benchmark Dataset

• How to get dataset?

1. APTOS 2019 Blindness : APTOS2019
2. ISIC2018 Challenge : ISIC2018
3. ISIC2018 Challenge : ISIC2017

• Directory structure of our project

- Directory
  - run.sh                : shell Script Version (pretraining, training, inference)
  - main.py               : Main Execution (Args, Setting)
  - dataset               : Augmentation, DataLoader
      - ...
  - learner               : training, inference 
      - ...
  - utils                 : Metrics, Jigsaw Setting (Metric, jigsaw Patch Setting)
      - ...

Supporting papers

(1) Jigsaw patch (Paper / Code)

(2) BarlowTwins (Paper / Code)

(3) progressive Learning (Paper / Code)

Training & Inference

python main.py --seed 0 --model pmg --patches 32 16 8 --dataset aptos --gpu_ids [gpu_idx]

The results will be automatically saved in ./workspace/[model name]/[Workspace Name].

How to cite

@article = {
    title = {Learning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization},
    author = {Wongi Park},
    journal = {GitHub},
    url = {https://github.com/kalelpark/FG-SSL},
    year = {2022},
}