Hybrid Representation Learning Approach for Rare Disease Classification

This repo contains the reference source code for the paper Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification in MICCAI2021. In this project, we provide a hybrid representation learning approach for rare disease classification. Our implementation is based on Pytorch.

This repository was built off of MoCo: Momentum Contrast for Unsupervised Visual Representation Learning.

Our extended version of the journal (Medical Image Analysis): Hybrid unsupervised representation learning and pseudo-label supervised self-distillation for rare disease imaging phenotype classification with dispersion-aware imbalance correction, can be found here: Hbr.Dst.-DIC

Prerequisites

Install python dependencies.

$ pip install -r requirements.txt

Data preparation

The 2018 skin lesion classification dataset are available here.

Run the code

(a) Unsupervised representation learning (URL)

Run unsupervised representation learning on the base dataset.

python main_moco.py \
 --arch resnet12 \
 --epochs 200 -b 16 -j 4 \
 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
 --moco-k 1280  \
 [data_folder] --model_path [path to save model]

Path flags:

data_folder: specify the data folder.
--model_path: specify the path to save model.

MoCo flags:

--moco-k: number of negatives to contrast for each positive. Default: 1280

(b)—(c) Generate pseudo labels

python generate_pseudo.py \
 --arch resnet12 \
 --n_way 3 --k_shot 5 --k_query 15 \
 --resume [pretrained_model_path]
 --datadir [data_folder] 
 --savedir [pseudo labels saving path]

Path flags:

--resume: specify the path of unsupervised pretrained model.
--datadir: specify the data folder.
--savedir: specify the path to save pseudo labels.

(d) Self-distillation via hybrid unsupervised and pseudo-label supervised representation learning

python main_distill.py \
 --arch resnet12 \
 --epochs 200 -b 16 -j 4 \
 --dist-url 'tcp://localhost:10002' --multiprocessing-distributed --world-size 1 --rank 0 \
 --moco-k 1280 \
 [data_folder] --model_path [model saving path] --savedir [pseudo labels saving path] \
 --p_label --n_way 3 --k_shot 5

Path flags:

data_folder: specify the data folder.
--model_path: specify the path to save model.
--savedir: specify the path of pseudo labels.

MoCo flags:

--moco-k: number of negatives to contrast for each positive. Default: 1280

Linear Classification

python -u test.py \
 --gpu [gpu_id] \
 --arch resnet12 \
 --n_way 3 --k_shot 5 \
 --load_cla \
 --resume [pretrained_model_path]
 [data_folder]

Path flags:

data_folder: specify the data folder.
resume: specify the path of trained model.

Citation

Please cite our paper if the code is helpful to your research.

@inproceedings{sun2021unsupervised,
  title={Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification},
  author={Sun, Jinghan and Wei, Dong and Ma, Kai and Wang, Liansheng and Zheng, Yefeng},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={519--529},
  year={2021},
  organization={Springer}
}

Concact

If you have any question, please feel free to concat Jinghan Sun (Email: jhsun@stu.xmu.edu.cn)

jinghanSunn/Hybrid-Representation-Learning-Approach-for-Rare-Disease-Classification

Hybrid Representation Learning Approach for Rare Disease Classification

Prerequisites

Data preparation

Run the code

Citation

Concact