Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

Peng Yao, Shuwei Shen, Mengjuan Xu, Peng Liu, Fan Zhang, Jinyu Xing, Pengfei Shao, Benjamin Kaffenberger, and Ronald X. Xu*

This repository is the official PyTorch implementation of paper Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification.

Main requirements

• torch == 1.5.1
• Python 3

Environmental settings

This repository is developed using python 3.6 on Ubuntu 16.04 LTS. The CUDA version is 9.2. For all experiments, we use two NVIDIA 2080ti GPU card for training and testing.

Usage

# To train ISIC 2018:
python main/train.py  --cfg configs/isic_2018.yaml

# To validate with the best model:
python main/valid.py  --cfg configs/isic_2018.yaml

You can change the experimental setting by simply modifying the parameter in the yaml file.

Data format

The annotation of a dataset is a dict consisting of two field: annotations and num_classes. The field annotations is a list of dict with image_id, category_id, derm_height, derm_width and derm_path.

Here is an example.

{
    'annotations': [
                    {
                        'image_id': ISIC_0031633,
                        'category_id':4,
                        'derm_height':450,
                        'derm_width':600,
                        'derm_path': '/work/image/skin_cancer/HAM10000/ISIC_0031633.jpg'
                    },
                    ...
                   ]
    'num_classes':7
}

You can use the following code to convert from the original format of Derm7PT, ISIC 2017, ISIC 2018 or ISIC 2019. The images and annotations of ISIC can be downloaded at ISIC. The images and annotations of Derm7PT can be downloaded at Derm7PT.

# Convert from the original format of ISIC 2017
python tools/convert_from_ISIC_2017.py --file ISIC_2017.csv --root /work/image/skin_cancer/ISIC_2017 --sp /work/skin_cancer/jsons

Contacts

If you have any questions about our work, please do not hesitate to contact us by email.

Peng Yao: yaopeng@ustc.edu.cn