Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (11): 3617-3622.DOI: 10.11772/j.issn.1001-9081.2023111650
• Multimedia computing and computer simulation • Previous Articles Next Articles
Cong GU(), Qiqiang DUAN, Siyu REN
Received:
2023-12-01
Revised:
2024-05-20
Accepted:
2024-05-21
Online:
2024-06-05
Published:
2024-11-10
Contact:
Cong GU
About author:
DUAN Qiqiang, born in 1999, M. S. candidate. His research interests include medical image processing, deep learning.Supported by:
通讯作者:
顾聪
作者简介:
段其强(1999—),男,河南濮阳人,硕士研究生,主要研究方向:医学图像处理、深度学习基金资助:
CLC Number:
Cong GU, Qiqiang DUAN, Siyu REN. Polyp segmentation algorithm based on context-aware network[J]. Journal of Computer Applications, 2024, 44(11): 3617-3622.
顾聪, 段其强, 任思雨. 基于上下文感知网络的息肉分割算法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3617-3622.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023111650
模型 | Kvasir-SEG数据集 | CVC-ClinicDB数据集 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
mDice | mIoU | m | MAE | mDice | mIoU | m | MAE | |||||
U-Net | 0.818 | 0.746 | 0.794 | 0.858 | 0.881 | 0.055 | 0.823 | 0.755 | 0.811 | 0.889 | 0.913 | 0.019 |
UNet++ | 0.821 | 0.743 | 0.808 | 0.862 | 0.886 | 0.048 | 0.794 | 0.729 | 0.785 | 0.873 | 0.891 | 0.022 |
SFA | 0.723 | 0.611 | 0.670 | 0.782 | 0.834 | 0.075 | 0.700 | 0.607 | 0.647 | 0.793 | 0.840 | 0.042 |
MSEG | 0.897 | 0.839 | 0.885 | 0.912 | 0.942 | 0.028 | 0.909 | 0.864 | 0.907 | 0.938 | 0.961 | 0.007 |
DCRNet | 0.886 | 0.825 | 0.868 | 0.911 | 0.933 | 0.035 | 0.896 | 0.844 | 0.890 | 0.933 | 0.964 | 0.010 |
ACSNet | 0.898 | 0.838 | 0.882 | 0.920 | 0.941 | 0.032 | 0.882 | 0.826 | 0.873 | 0.927 | 0.947 | 0.011 |
PraNet | 0.898 | 0.840 | 0.885 | 0.915 | 0.944 | 0.030 | 0.899 | 0.849 | 0.896 | 0.936 | 0.963 | 0.009 |
EU-Net | 0.908 | 0.854 | 0.893 | 0.917 | 0.951 | 0.028 | 0.902 | 0.846 | 0.891 | 0.936 | 0.959 | 0.011 |
SANet | 0.904 | 0.847 | 0.892 | 0.915 | 0.949 | 0.028 | 0.916 | 0.859 | 0.909 | 0.939 | 0.971 | 0.012 |
Polyp-PVT | 0.913 | 0.864 | 0.905 | 0.922 | 0.954 | 0.025 | 0.927 | 0.878 | 0.922 | 0.944 | 0.979 | 0.007 |
META-Unet | 0.896 | 0.837 | 0.884 | 0.909 | 0.942 | 0.032 | 0.909 | 0.856 | 0.905 | 0.939 | 0.963 | 0.011 |
CANet | 0.926 | 0.875 | 0.918 | 0.929 | 0.963 | 0.022 | 0.940 | 0.893 | 0.937 | 0.951 | 0.986 | 0.006 |
Tab. 1 Performance comparison of different models on Kvasir-SEG and CVC-ClinicDB dataset
模型 | Kvasir-SEG数据集 | CVC-ClinicDB数据集 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
mDice | mIoU | m | MAE | mDice | mIoU | m | MAE | |||||
U-Net | 0.818 | 0.746 | 0.794 | 0.858 | 0.881 | 0.055 | 0.823 | 0.755 | 0.811 | 0.889 | 0.913 | 0.019 |
UNet++ | 0.821 | 0.743 | 0.808 | 0.862 | 0.886 | 0.048 | 0.794 | 0.729 | 0.785 | 0.873 | 0.891 | 0.022 |
SFA | 0.723 | 0.611 | 0.670 | 0.782 | 0.834 | 0.075 | 0.700 | 0.607 | 0.647 | 0.793 | 0.840 | 0.042 |
MSEG | 0.897 | 0.839 | 0.885 | 0.912 | 0.942 | 0.028 | 0.909 | 0.864 | 0.907 | 0.938 | 0.961 | 0.007 |
DCRNet | 0.886 | 0.825 | 0.868 | 0.911 | 0.933 | 0.035 | 0.896 | 0.844 | 0.890 | 0.933 | 0.964 | 0.010 |
ACSNet | 0.898 | 0.838 | 0.882 | 0.920 | 0.941 | 0.032 | 0.882 | 0.826 | 0.873 | 0.927 | 0.947 | 0.011 |
PraNet | 0.898 | 0.840 | 0.885 | 0.915 | 0.944 | 0.030 | 0.899 | 0.849 | 0.896 | 0.936 | 0.963 | 0.009 |
EU-Net | 0.908 | 0.854 | 0.893 | 0.917 | 0.951 | 0.028 | 0.902 | 0.846 | 0.891 | 0.936 | 0.959 | 0.011 |
SANet | 0.904 | 0.847 | 0.892 | 0.915 | 0.949 | 0.028 | 0.916 | 0.859 | 0.909 | 0.939 | 0.971 | 0.012 |
Polyp-PVT | 0.913 | 0.864 | 0.905 | 0.922 | 0.954 | 0.025 | 0.927 | 0.878 | 0.922 | 0.944 | 0.979 | 0.007 |
META-Unet | 0.896 | 0.837 | 0.884 | 0.909 | 0.942 | 0.032 | 0.909 | 0.856 | 0.905 | 0.939 | 0.963 | 0.011 |
CANet | 0.926 | 0.875 | 0.918 | 0.929 | 0.963 | 0.022 | 0.940 | 0.893 | 0.937 | 0.951 | 0.986 | 0.006 |
模型 | mDice | mIoU | m | MAE | ||
---|---|---|---|---|---|---|
U-Net | 0.512 | 0.444 | 0.498 | 0.712 | 0.696 | 0.061 |
UNet++ | 0.483 | 0.410 | 0.467 | 0.691 | 0.680 | 0.064 |
SFA | 0.469 | 0.347 | 0.379 | 0.634 | 0.675 | 0.094 |
MSEG | 0.716 | 0.649 | 0.697 | 0.829 | 0.839 | 0.039 |
DCRNet | 0.735 | 0.666 | 0.724 | 0.834 | 0.859 | 0.038 |
ACSNet | 0.704 | 0.631 | 0.684 | 0.821 | 0.840 | 0.052 |
PraNet | 0.712 | 0.640 | 0.699 | 0.820 | 0.847 | 0.043 |
EU-Net | 0.756 | 0.681 | 0.730 | 0.831 | 0.863 | 0.045 |
SANet | 0.753 | 0.670 | 0.726 | 0.837 | 0.869 | 0.043 |
Polyp-PVT | 0.808 | 0.727 | 0.795 | 0.865 | 0.913 | 0.031 |
META-Unet | 0.751 | 0.675 | 0.737 | 0.836 | 0.881 | 0.038 |
CANet | 0.818 | 0.734 | 0.804 | 0.869 | 0.921 | 0.027 |
Tab. 2 Performance comparison of different models on CVC-ColonDB dataset
模型 | mDice | mIoU | m | MAE | ||
---|---|---|---|---|---|---|
U-Net | 0.512 | 0.444 | 0.498 | 0.712 | 0.696 | 0.061 |
UNet++ | 0.483 | 0.410 | 0.467 | 0.691 | 0.680 | 0.064 |
SFA | 0.469 | 0.347 | 0.379 | 0.634 | 0.675 | 0.094 |
MSEG | 0.716 | 0.649 | 0.697 | 0.829 | 0.839 | 0.039 |
DCRNet | 0.735 | 0.666 | 0.724 | 0.834 | 0.859 | 0.038 |
ACSNet | 0.704 | 0.631 | 0.684 | 0.821 | 0.840 | 0.052 |
PraNet | 0.712 | 0.640 | 0.699 | 0.820 | 0.847 | 0.043 |
EU-Net | 0.756 | 0.681 | 0.730 | 0.831 | 0.863 | 0.045 |
SANet | 0.753 | 0.670 | 0.726 | 0.837 | 0.869 | 0.043 |
Polyp-PVT | 0.808 | 0.727 | 0.795 | 0.865 | 0.913 | 0.031 |
META-Unet | 0.751 | 0.675 | 0.737 | 0.836 | 0.881 | 0.038 |
CANet | 0.818 | 0.734 | 0.804 | 0.869 | 0.921 | 0.027 |
模型 | mDice | mIoU | m | MAE | ||
---|---|---|---|---|---|---|
U-Net | 0.398 | 0.335 | 0.366 | 0.684 | 0.643 | 0.036 |
UNet++ | 0.401 | 0.344 | 0.390 | 0.683 | 0.629 | 0.035 |
SFA | 0.297 | 0.217 | 0.231 | 0.557 | 0.531 | 0.109 |
MSEG | 0.578 | 0.509 | 0.530 | 0.754 | 0.737 | 0.059 |
DCRNet | 0.700 | 0.630 | 0.671 | 0.828 | 0.854 | 0.015 |
ACSNet | 0.556 | 0.496 | 0.506 | 0.736 | 0.742 | 0.096 |
PraNet | 0.628 | 0.567 | 0.600 | 0.794 | 0.808 | 0.031 |
EU-Net | 0.687 | 0.609 | 0.636 | 0.793 | 0.807 | 0.067 |
SANet | 0.750 | 0.654 | 0.685 | 0.849 | 0.881 | 0.015 |
Polyp-PVT | 0.787 | 0.706 | 0.750 | 0.871 | 0.906 | 0.013 |
META-Unet | 0.650 | 0.574 | 0.611 | 0.793 | 0.831 | 0.032 |
CANet | 0.793 | 0.710 | 0.752 | 0.873 | 0.900 | 0.017 |
Tab. 3 Performance comparison of different models on ETIS dataset
模型 | mDice | mIoU | m | MAE | ||
---|---|---|---|---|---|---|
U-Net | 0.398 | 0.335 | 0.366 | 0.684 | 0.643 | 0.036 |
UNet++ | 0.401 | 0.344 | 0.390 | 0.683 | 0.629 | 0.035 |
SFA | 0.297 | 0.217 | 0.231 | 0.557 | 0.531 | 0.109 |
MSEG | 0.578 | 0.509 | 0.530 | 0.754 | 0.737 | 0.059 |
DCRNet | 0.700 | 0.630 | 0.671 | 0.828 | 0.854 | 0.015 |
ACSNet | 0.556 | 0.496 | 0.506 | 0.736 | 0.742 | 0.096 |
PraNet | 0.628 | 0.567 | 0.600 | 0.794 | 0.808 | 0.031 |
EU-Net | 0.687 | 0.609 | 0.636 | 0.793 | 0.807 | 0.067 |
SANet | 0.750 | 0.654 | 0.685 | 0.849 | 0.881 | 0.015 |
Polyp-PVT | 0.787 | 0.706 | 0.750 | 0.871 | 0.906 | 0.013 |
META-Unet | 0.650 | 0.574 | 0.611 | 0.793 | 0.831 | 0.032 |
CANet | 0.793 | 0.710 | 0.752 | 0.873 | 0.900 | 0.017 |
模型 | mDice | mIoU | m | MAE | ||
---|---|---|---|---|---|---|
U-Net | 0.710 | 0.627 | 0.684 | 0.843 | 0.847 | 0.022 |
UNet++ | 0.707 | 0.624 | 0.687 | 0.839 | 0.834 | 0.018 |
SFA | 0.467 | 0.329 | 0.341 | 0.640 | 0.644 | 0.065 |
MSEG | 0.874 | 0.804 | 0.852 | 0.924 | 0.948 | 0.009 |
DCRNet | 0.863 | 0.787 | 0.825 | 0.923 | 0.939 | 0.013 |
ACSNet | 0.856 | 0.788 | 0.830 | 0.921 | 0.943 | 0.010 |
PraNet | 0.871 | 0.797 | 0.843 | 0.925 | 0.950 | 0.010 |
EU-Net | 0.837 | 0.765 | 0.805 | 0.904 | 0.919 | 0.015 |
SANet | 0.888 | 0.815 | 0.859 | 0.928 | 0.962 | 0.008 |
Polyp-PVT | 0.900 | 0.833 | 0.884 | 0.935 | 0.973 | 0.007 |
META-Unet | 0.885 | 0.815 | 0.864 | 0.929 | 0.958 | 0.010 |
CANet | 0.889 | 0.820 | 0.868 | 0.929 | 0.963 | 0.007 |
Tab. 4 Performance comparison of different models on EndoScene dataset
模型 | mDice | mIoU | m | MAE | ||
---|---|---|---|---|---|---|
U-Net | 0.710 | 0.627 | 0.684 | 0.843 | 0.847 | 0.022 |
UNet++ | 0.707 | 0.624 | 0.687 | 0.839 | 0.834 | 0.018 |
SFA | 0.467 | 0.329 | 0.341 | 0.640 | 0.644 | 0.065 |
MSEG | 0.874 | 0.804 | 0.852 | 0.924 | 0.948 | 0.009 |
DCRNet | 0.863 | 0.787 | 0.825 | 0.923 | 0.939 | 0.013 |
ACSNet | 0.856 | 0.788 | 0.830 | 0.921 | 0.943 | 0.010 |
PraNet | 0.871 | 0.797 | 0.843 | 0.925 | 0.950 | 0.010 |
EU-Net | 0.837 | 0.765 | 0.805 | 0.904 | 0.919 | 0.015 |
SANet | 0.888 | 0.815 | 0.859 | 0.928 | 0.962 | 0.008 |
Polyp-PVT | 0.900 | 0.833 | 0.884 | 0.935 | 0.973 | 0.007 |
META-Unet | 0.885 | 0.815 | 0.864 | 0.929 | 0.958 | 0.010 |
CANet | 0.889 | 0.820 | 0.868 | 0.929 | 0.963 | 0.007 |
模型 | Kvasir-SEG | CVC-ColonDB | ||
---|---|---|---|---|
mDice | mIoU | mDice | mIoU | |
CANet | 0.926 | 0.875 | 0.818 | 0.734 |
CANet(w/oDADM) | 0.916 | 0.865 | 0.787 | 0.705 |
CANet(w/oSRM) | 0.904 | 0.848 | 0.806 | 0.727 |
CANet(w/oISEM) | 0.917 | 0.863 | 0.795 | 0.709 |
Tab. 5 Comparison results of ablation study
模型 | Kvasir-SEG | CVC-ColonDB | ||
---|---|---|---|---|
mDice | mIoU | mDice | mIoU | |
CANet | 0.926 | 0.875 | 0.818 | 0.734 |
CANet(w/oDADM) | 0.916 | 0.865 | 0.787 | 0.705 |
CANet(w/oSRM) | 0.904 | 0.848 | 0.806 | 0.727 |
CANet(w/oISEM) | 0.917 | 0.863 | 0.795 | 0.709 |
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