College of Software, Xinjiang University, Urumqi, 830046, China.
The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, 830011, China.
Sci Rep. 2022 Sep 6;12(1):15103. doi: 10.1038/s41598-022-18879-1.
Histopathological image analysis is the gold standard for pathologists to grade colorectal cancers of different differentiation types. However, the diagnosis by pathologists is highly subjective and prone to misdiagnosis. In this study, we constructed a new attention mechanism named MCCBAM based on channel attention mechanism and spatial attention mechanism, and developed a computer-aided diagnosis (CAD) method based on CNN and MCCBAM, called HCCANet. In this study, 630 histopathology images processed with Gaussian filtering denoising were included and gradient-weighted class activation map (Grad-CAM) was used to visualize regions of interest in HCCANet to improve its interpretability. The experimental results show that the proposed HCCANet model outperforms four advanced deep learning (ResNet50, MobileNetV2, Xception, and DenseNet121) and four classical machine learning (KNN, NB, RF, and SVM) techniques, achieved 90.2%, 85%, and 86.7% classification accuracy for colorectal cancers with high, medium, and low differentiation levels, respectively, with an overall accuracy of 87.3% and an average AUC value of 0.9.In addition, the MCCBAM constructed in this study outperforms several commonly used attention mechanisms SAM, SENet, SKNet, Non_Local, CBAM, and BAM on the backbone network. In conclusion, the HCCANet model proposed in this study is feasible for postoperative adjuvant diagnosis and grading of colorectal cancer.
组织病理学图像分析是病理学家对不同分化类型的结直肠癌进行分级的金标准。然而，病理学家的诊断具有高度主观性，容易出现误诊。在本研究中，我们构建了一种新的注意力机制，命名为 MCCBAM，基于通道注意力机制和空间注意力机制，并基于 CNN 和 MCCBAM 开发了一种计算机辅助诊断 (CAD) 方法，称为 HCCANet。在本研究中，共纳入了 630 张经过高斯滤波降噪处理的组织病理学图像，并使用梯度加权类激活图（Grad-CAM）可视化 HCCANet 中的感兴趣区域，以提高其可解释性。实验结果表明，所提出的 HCCANet 模型优于四个先进的深度学习（ResNet50、MobileNetV2、Xception 和 DenseNet121）和四个经典机器学习（KNN、NB、RF 和 SVM）技术，对高、中、低分化水平的结直肠癌分别实现了 90.2%、85%和 86.7%的分类准确率，总体准确率为 87.3%，平均 AUC 值为 0.9。此外，在骨干网络上，本文构建的 MCCBAM 优于几种常用的注意力机制 SAM、SENet、SKNet、Non_Local、CBAM 和 BAM。总之，本研究提出的 HCCANet 模型可用于结直肠癌的术后辅助诊断和分级。
