Predict the ISUP Grade of Clear Cell Renal Cell Carcinoma Using Pathological Images Based on sECANet Chanel Attention

YANG Kun; CHANG Shilong; WANG Yucheng; GAO Cong; LIU Xiao; LIU Shuang; XUE Linyan

doi:10.11999/JEIT210900

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 138-148

YANG Kun, CHANG Shilong, WANG Yucheng, GAO Cong, LIU Xiao, LIU Shuang, XUE Linyan. Predict the ISUP Grade of Clear Cell Renal Cell Carcinoma Using Pathological Images Based on sECANet Chanel Attention[J]. Journal of Electronics & Information Technology, 2022, 44(1): 138-148. doi: 10.11999/JEIT210900

Citation:

YANG Kun, CHANG Shilong, WANG Yucheng, GAO Cong, LIU Xiao, LIU Shuang, XUE Linyan. Predict the ISUP Grade of Clear Cell Renal Cell Carcinoma Using Pathological Images Based on sECANet Chanel Attention[J]. Journal of Electronics & Information Technology, 2022, 44(1): 138-148. doi: 10.11999/JEIT210900

Citation:

YANG Kun, CHANG Shilong, WANG Yucheng, GAO Cong, LIU Xiao, LIU Shuang, XUE Linyan. Predict the ISUP Grade of Clear Cell Renal Cell Carcinoma Using Pathological Images Based on sECANet Chanel Attention[J]. Journal of Electronics & Information Technology, 2022, 44(1): 138-148. doi: 10.11999/JEIT210900

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Predict the ISUP Grade of Clear Cell Renal Cell Carcinoma Using Pathological Images Based on sECANet Chanel Attention

doi: 10.11999/JEIT210900

YANG Kun^{1, 2, 3},
CHANG Shilong¹,
WANG Yucheng¹,
GAO Cong¹,
LIU Xiao¹,
LIU Shuang^{1, 2, 3},
XUE Linyan^{1, 2, 3
,
,}

1.
College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
2.
National & Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China
3.
Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding 071002, China

Funds: The Natural Science Foundation of Hebei Province (H2019201378)

Received Date: 2021-08-30
Accepted Date: 2021-12-27
Rev Recd Date: 2021-12-26

Available Online: 2022-01-04

Publish Date: 2022-01-10

Abstract

Abstract

In order to determine accurately International Society for Urology and Pathology (ISUP) grade of clear cell Renal Cell Carcinoma (ccRCC) and achieve subsequently better treatment and prognosis, a novel channel attention mechanism named sECANet is proposed. To obtain more useful features from the feature map, sECANet calculates the information interaction of the current channel and local channels, and calculates additionally the interaction of the current channel and remote channels. A total of 90 pathological images are collected and subsequently cut into patches. After data augmentation, 5-fold cross-validation is employed to verify the improved network at the patch level. The experiment results show that the proposed model achieves 78.48±3.17% accuracy, 79.95±4.37% precision, 78.43±2.44% recall and 78.51±3.04% F1-score for ccRCC grading at the patch level. Furthermore, for the prediction of all patches in each patient case, the majority voting method is used to obtain the overall classification of the image level. The accuracy, precision, recall and F1-score of the proposed model at the image level are 88.89%, 89.88%, 87.65%, and 88.51%, respectively. In conclusion, the improved network with sECANet outperforms other attention mechanisms and the baseline model of ResNet50 at both patch level and image level. Therefore, the model of ccRCC ISUP grade established in this paper has relatively high diagnostic efficiency, and can even provide a certain reference for the treatment and prognosis for ccRCC patients.
- Clear cell renal cell carcinoma,
- ISUP grade,
- Pathological images,
- Deep learning,
- Attention mechanism

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References(24)

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