Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (3): 818-824.DOI: 10.11772/j.issn.1001-9081.2021040948
• 2021 CCF Conference on Artificial Intelligence (CCFAI 2021) • Previous Articles
Shengqin LUO1, Jinyi CHEN1, Hongjun LI1,2()
Received:
2021-06-04
Revised:
2021-06-22
Accepted:
2021-06-29
Online:
2021-11-09
Published:
2022-03-10
Contact:
Hongjun LI
About author:
LUO Shengqin, born in 1997, M. S. candidate. His research interests include deep learning, medical image processing.Supported by:
通讯作者:
李洪均
作者简介:
罗圣钦(1997—),男,江苏盐城人,硕士研究生,CCF会员,主要研究方向:深度学习、医学图像处理基金资助:
CLC Number:
Shengqin LUO, Jinyi CHEN, Hongjun LI. Multiscale residual UNet based on attention mechanism to realize breast cancer lesion segmentation[J]. Journal of Computer Applications, 2022, 42(3): 818-824.
罗圣钦, 陈金怡, 李洪均. 基于注意力机制的多尺度残差UNet实现乳腺癌灶分割[J]. 《计算机应用》唯一官方网站, 2022, 42(3): 818-824.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021040948
方法 | Dice | IoU | 特异度 | 敏感度 | 准确率 |
---|---|---|---|---|---|
SegNet[ | 75.28 | 65.68 | 75.43 | 99.92 | 99.81 |
FCN-16s[ | 79.54 | 69.53 | 79.33 | 99.94 | 99.82 |
UNet[ | 80.98 | 72.13 | 80.82 | 99.94 | 99.81 |
ResUNet[ | 81.28 | 72.27 | 81.97 | 99.94 | 99.85 |
HarDNet-MSEG[ | 71.90 | 62.91 | 84.69 | 99.93 | 99.85 |
PraNet[ | 72.77 | 64.44 | 80.18 | 99.94 | 99.84 |
本文方法 | 83.24 | 74.24 | 84.98 | 99.93 | 99.86 |
Tab.1 Segmentation results of different networks
方法 | Dice | IoU | 特异度 | 敏感度 | 准确率 |
---|---|---|---|---|---|
SegNet[ | 75.28 | 65.68 | 75.43 | 99.92 | 99.81 |
FCN-16s[ | 79.54 | 69.53 | 79.33 | 99.94 | 99.82 |
UNet[ | 80.98 | 72.13 | 80.82 | 99.94 | 99.81 |
ResUNet[ | 81.28 | 72.27 | 81.97 | 99.94 | 99.85 |
HarDNet-MSEG[ | 71.90 | 62.91 | 84.69 | 99.93 | 99.85 |
PraNet[ | 72.77 | 64.44 | 80.18 | 99.94 | 99.84 |
本文方法 | 83.24 | 74.24 | 84.98 | 99.93 | 99.86 |
方法 | Dice | IoU | 特异度 | 敏感度 | 准确率 |
---|---|---|---|---|---|
UNet | 80.98 | 72.13 | 80.82 | 99.94 | 99.81 |
UNet+LCA | 81.32 | 72.29 | 81.98 | 99.94 | 99.85 |
UNet+ASPP | 81.42 | 72.35 | 81.81 | 99.94 | 99.85 |
UNet+MRCB | 81.59 | 72.46 | 82.13 | 99.93 | 99.85 |
UNet+ASPP+LCA | 81.34 | 72.37 | 82.33 | 99.94 | 99.85 |
UNet+ASPP+MRCB | 82.21 | 73.01 | 83.09 | 99.95 | 99.86 |
UNet+MRCB+LCA | 81.85 | 72.96 | 82.60 | 99.30 | 99.85 |
本文方法 | 83.24 | 74.24 | 84.98 | 99.93 | 99.86 |
Tab.2 Comparison of network structure ablation
方法 | Dice | IoU | 特异度 | 敏感度 | 准确率 |
---|---|---|---|---|---|
UNet | 80.98 | 72.13 | 80.82 | 99.94 | 99.81 |
UNet+LCA | 81.32 | 72.29 | 81.98 | 99.94 | 99.85 |
UNet+ASPP | 81.42 | 72.35 | 81.81 | 99.94 | 99.85 |
UNet+MRCB | 81.59 | 72.46 | 82.13 | 99.93 | 99.85 |
UNet+ASPP+LCA | 81.34 | 72.37 | 82.33 | 99.94 | 99.85 |
UNet+ASPP+MRCB | 82.21 | 73.01 | 83.09 | 99.95 | 99.86 |
UNet+MRCB+LCA | 81.85 | 72.96 | 82.60 | 99.30 | 99.85 |
本文方法 | 83.24 | 74.24 | 84.98 | 99.93 | 99.86 |
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