Gland and Colonoscopy Segmentation Method Combining Self-Attention and Convolutional Neural Network

Table 1. Results of ablation experiment
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Table 1. Results of ablation experiment
Model Dice IOU
Test A Test B Test A Test B
U-Net 0.8826 0.8269 0.7934 0.7216
Local UNet 0.9133 0.8746 0.8447 0.7868
LG-v1 0.9267 0.8822 0.8663 0.8010
LG-v2 0.9302 0.8834 0.8711 0.8028
LG-v3 0.9362 0.8844 0.8817 0.8049

Table 2. Double channel and single channel output results are compared
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Table 2. Double channel and single channel output results are compared
Model Dice IOU
Test A Test B Test A Test B
U-Net -2 0.8826 0.8269 0.7934 0.7216
U-Net -1 0.8819 0.8539 0.7956 0.7606
LG UNet -2 0.9362 0.8844 0.8817 0.8049
LG UNet -1 0.9100 0.8663 0.8380 0.7858

Table 3. Comparison results with other algorithms on Glas dataset

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Table 3. Comparison results with other algorithms on Glas dataset

Model	Dice			IOU
Model	Test A	Test B	Mean	Test A	Test B	Mean
U-Net	0.8826	0.8269	0.8547	0.7934	0.7216	0.7575
ResUNet	0.8940	0.8459	0.8699	0.8146	0.7461	0.7803
KiU-Net^［28］	0.8898	0.8527	0.8712	0.8065	0.7565	0.7815
MedT	0.8674	0.8051	0.8362	0.7711	0.6889	0.7300
Rota-Net^［29］	0.919	0.849	0.884
U-Node^［30］	0.8930	0.8420	0.8675
LG UNet	0.9362	0.8844	0.9103	0.8817	0.8049	0.8433

Table 4. Comparison results with other algorithms on Kvasir-SEG dataset
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Table 4. Comparison results with other algorithms on Kvasir-SEG dataset
Model Dice IOU
Test A Test A
U-Net 0.8110 0.7250
KiU-Net^［28］ 0.7724 0.6689
ResUNet 0.8306 0.7477
ResUNet++ 0.8508 0.7699
ColonSegNet^［31］ 0.8206
MedT 0.8039 0.7116
LG UNet 0.8563 0.7782

Table 5. Comparisons on other data sets
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Table 5. Comparisons on other data sets
Model ClinicDB NoMuSeg
Dice IOU Dice IOU
U-Net 0.7926 0.7115 0.7930 0.7188
LG UNet 0.8607 0.7946 0.8317 0.7126

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Jiabao Zhang, Zhiyong Xiao. Gland and Colonoscopy Segmentation Method Combining Self-Attention and Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0217002

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