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https://github.com/enkiwang/portable-skin-lesion-diagnosis
SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images
https://github.com/enkiwang/portable-skin-lesion-diagnosis
deep-learning knowledge-distillation lightweight-skin-lesion-model skin-cancer-diagnosis
Last synced: 6 days ago
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SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images
- Host: GitHub
- URL: https://github.com/enkiwang/portable-skin-lesion-diagnosis
- Owner: enkiwang
- License: mit
- Created: 2022-03-06T07:38:21.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-04-11T03:37:21.000Z (over 1 year ago)
- Last Synced: 2023-07-30T14:21:15.755Z (over 1 year ago)
- Topics: deep-learning, knowledge-distillation, lightweight-skin-lesion-model, skin-cancer-diagnosis
- Language: Python
- Homepage:
- Size: 71.3 KB
- Stars: 5
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Portable-Skin-Lesion-Diagnosis
This repository provides our codes to train a portable skin lesion diagnosis model based on our proposed knowledge distillation framework. The portable student model is trained by distilling, integrating, and transferring diverse knowledge (D-KD) extracted from a pretrained teacher model. A self-supervised variant (SSD-KD) is also employed to guide the student to capture richer informative knowledge for skin lesion diagnosis.
## Environment configuration
Our KD implementations were tested under a linux-centos7-x86\_64 system with GPU cards as NVIDIA Tesla V100 (16GB/32GB memory). Please refer to python dependencies in the `requirements.txt` file. You can install these required dependencies via,
```python
pip3 install -r requirements.txt
```
## Dataset preparation
Please download ISIC 2019, a large-scale dermoscopic image dataset, from [this link](https://challenge2019.isic-archive.com/). After downloading ISIC 2019, please put it in the data/ folder, then preprocess this dataset using the provided script,
```bash
bash ./run_preprocess.sh
```
## Train a portable diagnosis model
Please go to the experiment/isic/ directory, download the teacher model ResNet-50 pretrained by us in [Google Drive](https://drive.google.com/file/d/1yz0nh3811KoyVqz_ln_JsLdC--ssAQD6/view?usp=sharing), and put it in the results/ folder. You can also train it from scratch using `isic.py` which was borrowed from the [MetaBlock](https://github.com/paaatcha/MetaBlock/tree/main/benchmarks/isic) project.
To train the portable student MobileNet-V2 model, you can choose to use D-KD or SSD-KD:
* For the D-KD method, please perform training following a demo,
```python
python3 isic_d_kd.py with '_kd_method="d_kd"' '_lambd_drkd=1' '_lambd_crkd=1000'
```
You can also download our trained student model by D-KD from [Google Drive](https://drive.google.com/file/d/1b5Wl3lQM5qmsQvftQjy7aQWN_y5Ha8er/view?usp=sharing).
* For the SSD-KD method, please perform training following a demo,
```python
python3 isic_ssd_kd.py with '_kd_method="ssd_kd"' '_lambd_drkd=1' '_lambd_crkd=1000'
```
You can also download our trained student model by SSD-KD from [Google Drive](https://drive.google.com/file/d/1UkRX0c2moP906tqcaT9hCiS9WaoWJdld/view?usp=sharing).
If you encounter possible issues regarding this code, please do not hesitate to [contact me](mailto:[email protected]).
If you find our code useful in your research, please consider citing our work:
```bib
@article{wang2023ssd,
title={Ssd-kd: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images},
author={Wang, Yongwei and Wang, Yuheng and Cai, Jiayue and Lee, Tim K and Miao, Chunyan and Wang, Z Jane},
journal={Medical Image Analysis},
volume={84},
pages={102693},
year={2023},
publisher={Elsevier}
}
```
## Acknowledgements
Many thanks to the following open-sourced repositories that we borrowed which have greatly facilitated our implementations:
* [https://github.com/paaatcha/MetaBlock]
* [https://github.com/paaatcha/raug]
* [https://raw.githubusercontent.com/AberHu/Knowledge-Distillation-Zoo/]
* [https://github.com/xuguodong03/SSKD]