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

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...

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

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.