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
Our program is easy to train and test, just need to run "main_train.py".
python main_train.py
Method | Para(M) | Flops (G) | JI | DSC | ACC |
---|---|---|---|---|---|
FCN | 15.31 | 21.98 | 78.66±0.41 | 86.80±0.32 | 95.04±0.32 |
U-Net | 34.53 | 71.61 | 81.69±0.50 | 88.81±0.40 | 95.68±0.29 |
U-Net++ | 36.63 | 151.59 | 81.87±0.47 | 88.93±0.38 | 95.68±0.33 |
AttU-Net | 34.88 | 72.81 | 81.99±0.59 | 89.03±0.42 | 95.77±0.26 |
DeepLabv3+ | 39.76 | 47.34 | 82.32±0.35 | 89.26±0.23 | 95.87±0.23 |
DenseASPP | 35.37 | 42.63 | 82.53±0.55 | 89.35±0.37 | 95.89±0.28 |
BCDU-Net | 28.8 | 171.50 | 80.84±0.57 | 88.33±0.48 | 95.48±0.40 |
Focus-Alpha | 26.36 | 41.92 | 81.92±0.63 | 88.93±0.41 | 95.84±0.44 |
DO-Net | 24.75 | 122.45 | 82.61±0.51 | 89.48±0.37 | 95.78±0.36 |
CE-Net | 29.00 | 9.75 | 82.82±0.45 | 89.59±0.35 | 95.97±0.30 |
CPF-Net | 30.65 | 8.78 | 82.92±0.52 | 89.63±0.42 | 96.02±0.34 |
MSCA-Net (Ours) | 27.09 | 12.88 | 84.18±0.38 | 90.52±0.26 | 96.41±0.29 |
If you find our work is helpful for your research, please consider to cite:
@article{sun2023msca,
title={MSCA-Net: Multi-scale contextual attention network for skin lesion segmentation},
author={Sun, Yongheng and Dai, Duwei and Zhang, Qianni and Wang, Yaqi and Xu, Songhua and Lian, Chunfeng},
journal={Pattern Recognition},
pages={109524},
year={2023},
publisher={Elsevier}
}