Awesome

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}
}