Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2955-2962.DOI: 10.11772/j.issn.1001-9081.2022081159
• Multimedia computing and computer simulation • Previous Articles Next Articles
Di ZHOU1,2, Zili ZHANG1,2(), Jia CHEN1,3, Xinrong HU2,3, Ruhan HE2,3, Jun ZHANG4
Received:
2022-08-07
Revised:
2022-11-03
Accepted:
2022-11-14
Online:
2023-01-11
Published:
2023-09-10
Contact:
Zili ZHANG
About author:
ZHOU Di, born in 1997, M. S. candidate, His research interests include machine learning, image processing.Supported by:
周迪1,2, 张自力1,2(), 陈佳1,3, 胡新荣2,3, 何儒汉2,3, 张俊4
通讯作者:
张自力
作者简介:
周迪(1997—),男,湖北武汉人,硕士研究生,CCF会员,主要研究方向:机器学习、图像处理基金资助:
CLC Number:
Di ZHOU, Zili ZHANG, Jia CHEN, Xinrong HU, Ruhan HE, Jun ZHANG. Stomach cancer image segmentation method based on EfficientNetV2 and object-contextual representation[J]. Journal of Computer Applications, 2023, 43(9): 2955-2962.
周迪, 张自力, 陈佳, 胡新荣, 何儒汉, 张俊. 基于EfficientNetV2和物体上下文表示的胃癌图像分割方法[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2955-2962.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022081159
Stage | Operator | 编码器是否输出特征图 |
---|---|---|
0 | Conv3×3 | |
1 | Fused-MBConv1,k 3×3 | — |
2 | Fused-MBConv4,k 3×3 | |
3 | Fused-MBConv4,k 3×3 | |
4 | MBConv4,k 3×3,SE 0.25 | |
5 | MBConv6,k 3×3,SE 0.25 | — |
6 | MBConv6,k 3×3,SE 0.25 | |
Tab. 1 Basic modules of EfficientNetV2
Stage | Operator | 编码器是否输出特征图 |
---|---|---|
0 | Conv3×3 | |
1 | Fused-MBConv1,k 3×3 | — |
2 | Fused-MBConv4,k 3×3 | |
3 | Fused-MBConv4,k 3×3 | |
4 | MBConv4,k 3×3,SE 0.25 | |
5 | MBConv6,k 3×3,SE 0.25 | — |
6 | MBConv6,k 3×3,SE 0.25 | |
EfficientNetV2 | OCR | TTA | MIoU |
---|---|---|---|
— | — | — | 80.1 |
| — | — | 80.5 |
| | — | 80.8 |
| | | 81.4 |
Tab. 2 Ablation experimental results of EOU-Net
EfficientNetV2 | OCR | TTA | MIoU |
---|---|---|---|
— | — | — | 80.1 |
| — | — | 80.5 |
| | — | 80.8 |
| | | 81.4 |
方法 | 图像增强 | 特征融合 | MIoU |
---|---|---|---|
TTA | 垂直翻转 | 平均 | 81.20 |
几何平均 | 79.50 | ||
相加 | 81.20 | ||
水平翻转 | 平均 | 81.20 | |
几何平均 | 79.50 | ||
相加 | 81.20 | ||
水平垂直翻转 | 平均 | 81.30 | |
几何平均 | 78.00 | ||
相加 | 81.30 | ||
水平垂直翻转+旋转 | 平均 | 81.40 | |
几何平均 | 76.70 | ||
相加 | 81.40 | ||
DenseCRF-3 | — | — | 80.53 |
DenseCRF-5 | — | — | 80.49 |
DenseCRF-7 | — | — | 80.45 |
Tab. 3 Comparisons of different post-processing methods
方法 | 图像增强 | 特征融合 | MIoU |
---|---|---|---|
TTA | 垂直翻转 | 平均 | 81.20 |
几何平均 | 79.50 | ||
相加 | 81.20 | ||
水平翻转 | 平均 | 81.20 | |
几何平均 | 79.50 | ||
相加 | 81.20 | ||
水平垂直翻转 | 平均 | 81.30 | |
几何平均 | 78.00 | ||
相加 | 81.30 | ||
水平垂直翻转+旋转 | 平均 | 81.40 | |
几何平均 | 76.70 | ||
相加 | 81.40 | ||
DenseCRF-3 | — | — | 80.53 |
DenseCRF-5 | — | — | 80.49 |
DenseCRF-7 | — | — | 80.45 |
数据集 | 模型 | MIoU | 不同种类的IoU | |
---|---|---|---|---|
正常 | 病变 | |||
SEED | Att R2U-Net* | 71.2 | 72.1 | 70.3 |
Att U-Net* | 74.3 | 76.5 | 72.2 | |
EOU-Net* | 76.5 | 78.3 | 74.9 | |
U-Net | 80.1 | 81.4 | 78.9 | |
U-Net++ | 78.2 | 79.2 | 77.2 | |
DeepLabV3+[ | 79.7 | 81.2 | 78.2 | |
OCRNet[ | 79.6 | 80.8 | 78.5 | |
EOU-Net | 81.4 | 82.5 | 80.3 | |
BOT | Att R2U-Net* | 61.8 | 88.7 | 34.9 |
Att U-Net* | 67.3 | 88.5 | 46.0 | |
EOU-Net* | 68.5 | 89.2 | 47.8 | |
U-Net | 73.0 | 90.7 | 55.3 | |
U-Net++ | 72.8 | 90.5 | 55.1 | |
DeepLabV3+[ | 73.1 | 90.3 | 55.9 | |
OCRNet[ | 74.8 | 91.1 | 58.5 | |
EOU-Net | 75.4 | 91.4 | 59.4 |
Tab. 4 Comparison experimental results on SEED and BOT datasets
数据集 | 模型 | MIoU | 不同种类的IoU | |
---|---|---|---|---|
正常 | 病变 | |||
SEED | Att R2U-Net* | 71.2 | 72.1 | 70.3 |
Att U-Net* | 74.3 | 76.5 | 72.2 | |
EOU-Net* | 76.5 | 78.3 | 74.9 | |
U-Net | 80.1 | 81.4 | 78.9 | |
U-Net++ | 78.2 | 79.2 | 77.2 | |
DeepLabV3+[ | 79.7 | 81.2 | 78.2 | |
OCRNet[ | 79.6 | 80.8 | 78.5 | |
EOU-Net | 81.4 | 82.5 | 80.3 | |
BOT | Att R2U-Net* | 61.8 | 88.7 | 34.9 |
Att U-Net* | 67.3 | 88.5 | 46.0 | |
EOU-Net* | 68.5 | 89.2 | 47.8 | |
U-Net | 73.0 | 90.7 | 55.3 | |
U-Net++ | 72.8 | 90.5 | 55.1 | |
DeepLabV3+[ | 73.1 | 90.3 | 55.9 | |
OCRNet[ | 74.8 | 91.1 | 58.5 | |
EOU-Net | 75.4 | 91.4 | 59.4 |
模型 | MIoU | 模型 | MIoU |
---|---|---|---|
U-Net | 46.5 | DeepLabV3+[ | 67.4 |
FCN[ | 62.7 | OCRNet[ | 72.3 |
PSPNet[ | 66.8 | EOU-Net | 76.8 |
Tab. 5 Comparison results on PASCAL VOC 2012 dataset
模型 | MIoU | 模型 | MIoU |
---|---|---|---|
U-Net | 46.5 | DeepLabV3+[ | 67.4 |
FCN[ | 62.7 | OCRNet[ | 72.3 |
PSPNet[ | 66.8 | EOU-Net | 76.8 |
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