Ping XIA, Guangyi ZHANG, Bangjun LEI, Yaobing ZOU, Tinglong TANG. Polyp image segmentation based on multi-scale ResNeSt-50 aggregation network and message passing[J]. Optics and Precision Engineering, 2023, 31(18): 2765

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- Optics and Precision Engineering
- Vol. 31, Issue 18, 2765 (2023)

Fig. 1. Model of this paper

Fig. 2. Split-attention in ResNeSt Block

Fig. 3. ResNeSt-50 encoding structure of this paper

Fig. 4. RFB and dense aggregation module

Fig. 5. Message propagation of confidence level

Fig. 6. Segmentation result of testing on the ETIS-LaribPolypDB dataset
![Segmentation result of testing on the Kvasir-SEG[34] dataset](/Images/icon/loading.gif)
Fig. 7. Segmentation result of testing on the Kvasir-SEG[34] dataset
![Segmentation result of testing small lesion on ETIS-LaribPolypDB[1]dataset](/Images/icon/loading.gif)
Fig. 8. Segmentation result of testing small lesion on ETIS-LaribPolypDB[1]dataset

Fig. 9. Comparison of the effect of the model used in this paper and PraNet model in segmenting minimal lesions
![Segmentation result of testing on the ETIS-LaribPolypDB[1]dataset](/Images/icon/loading.gif)
Fig. 10. Segmentation result of testing on the ETIS-LaribPolypDB[1]dataset
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Table 1. Five datasets used in this paper
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Table 2. Allocation of training dataset and test dataset of experiment
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Table 3. Ablation experiment of ResNeSt-50 module(training on Kvasir-SEG and ClinicDB dataset)
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Table 4. Effect of multi-scale training on model segmentation
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Table 5. Effect of RAdam optimizer on model segmentation
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Table 6. Trained on CVC-ClinicDB[33],Kvasir-SEG[34] dataset, compare the test results on Kvasir-SEG[34] dataset
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Table 7. Trained on the CVC-ClinicDB[33],Kvasir-SEG[34]dataset, respectively compare the test results on ColonDB[35],ETIS-LaribPolypDB[1],CVC-300[36]
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Table 8. Trained on CVC-ClinicDB[33],Kvasir-SEG[34] dataset, compare the test results on Kvasir-SEG[34] dataset
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Table 9. Trained on CVC-ClinicDB,Kvasir-SEGdataset, compare the test results on ETIS-LaribPolypDBdataset
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