基于sECANet通道注意力机制的肾透明细胞癌病理图像ISUP分级预测  

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

作　　者：杨昆[1,2,3] 常世龙 王尉丞 高聪 刘筱 刘爽 薛林雁[1,2,3] YANG Kun;CHANG Shilong;WANG Yucheng;GAO Cong;LIU Xiao;LIU Shuang;XUE Linyan(College of Quality and Technical Supervision,Hebei University,Baoding 071002,China;National&Local Joint Engineering Research Center of Metrology Instrument and System,Hebei University,Baoding 071002,China;Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System,Baoding 071002,China)

机构地区：[1]河北大学质量技术监督学院,保定071002 [2]计量仪器与系统国家地方联合工程研究中心,保定071002 [3]河北省新能源汽车动力系统轻量化技术创新中心,保定071002

出　　处：《电子与信息学报》2022年第1期138-148,共11页Journal of Electronics & Information Technology

基　　金：河北省自然科学基金(H2019201378)。

摘　　要：为了对肾透明细胞癌(ccRCC)进行准确核分级以改善肾癌的治疗和预后,该文提出一种新的通道注意力模块sECANet,通过计算特征图中当前通道与临近通道以及当前通道与远距离通道之间的信息交互来获取更多有用的特征。实验中收集了90例患者的肾组织病理图像,进行裁切和增强后采用五折交叉验证法对改进后的网络在Patch级别进行验证。实验结果表明,该文所提出的模型在Patch级别上鉴别ISUP分级的准确率为78.48±3.17%,精确率为79.95±4.37%,召回率为78.43±2.44%,F1分数为78.51±3.04%。进一步地,对每个病例所有Patch的预测结果采用多数投票法得到Image级别的分类结果,所有病例的准确率为88.89%,精确率为89.88%,召回率为87.65%,F1分数为88.51%。因此,sECANet在Patch级别和Image级别上均优于其他注意力机制和基本网络模型ResNet50。据此,该文所构建的病理图像ccRCC ISUP分级模型有良好的诊断效能,可以为患者的治疗和预后提供一定的参考。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.

关 键 词：肾透明细胞癌 ISUP分级 病理图像 深度学习 注意力机制 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391.72[电子电信—信息与通信工程]