/Ms-RED

Code is soon coming!

Primary LanguagePython

Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation

https://www.sciencedirect.com/science/article/pii/S1361841521003388

Data preparation

We cropped the ISIC 2018 dataset to 224*320 and saved it in npy format, which can be downloaded from Baidu web disk.

link: https://pan.baidu.com/s/1bIVUdzYG_7tuwalbI4Y8Ww

password: c36c

Place the downloaded npy files in the "data" directory and unzip them. The decompression format is as follows:

/data/ISIC2018_npy_all_224_320/image/

​		ISIC_0000000.npy

​		ISIC_0000001.npy

​		...

​		ISIC_0016072.npy

/data/ISIC2018_npy_all_224_320/label/

​		ISIC_0000000_segmentation.npy

​		ISIC_0000001_segmentation.npy

​		......

​		ISIC_0016072_segmentation.npy

Train and Test

Our program is easy to train and test, just need to run "main_train.py".

python main_train.py

Performance on ISIC 2018

Networks Para(M) JI Dice ACC Recall Precision
FCN 14.6 0.7866 0.8680 0.9504 0.8827 0.8784
U-Net 32.9 0.8169 0.8881 0.9568 0.8858 0.9131
U-Net++ 34.9 0.8187 0.8893 0.9568 0.8910 0.9098
AttU-Net 33.3 0.8199 0.8903 0.9577 0.8898 0.9126
DeepLabv3+ 37.9 0.8232 0.8926 0.9587 0.8974 0.9087
DenseASPP 33.7 0.8253 0.8935 0.9589 0.8950 0.9138
CA-Net 2.7 0.8041 0.8782 0.9525 0.8762 0.9072
BCDU-Net 28.8 0.8084 0.8833 0.9548 0.8913 0.8968
Focus-Alpha 26.4 0.8192 0.8893 0.9584 0.9157 0.8860
DO-Net 24.7 0.8261 0.8948 0.9578 0.9036 0.9059
CE-Net 29.0 0.8282 0.8959 0.9597 0.9054 0.9067
CPF-Net 43.3 0.8292 0.8963 0.9602 0.9062 0.9071
Ms RED (our） 3.8 0.8345 0.8999 0.9619 0.9049 0.9147

Reference

@article{dai2022ms,
  title={Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation},
  author={Dai, Duwei and Dong, Caixia and Xu, Songhua and Yan, Qingsen and Li, Zongfang and Zhang, Chunyan and Luo, Nana},
  journal={Medical Image Analysis},
  volume={75},
  pages={102293},
  year={2022},
  publisher={Elsevier}
}