Han Zixin, Lan Junlin, Wang Tao, Hu Ziwei, Huang Yuxiu, Deng Yanglin, Zhang Hejun, Wang Jianchao, Chen Musheng, Jiang Haiyan, Lee Ren-Guey, Gao Qinquan, Du Ming, Tong Tong, Chen Gang
College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China.
Front Neurosci. 2022 May 30;16:877229. doi: 10.3389/fnins.2022.877229. eCollection 2022.
Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.
胃癌是全球癌症相关死亡的第三大常见原因。人表皮生长因子受体2（HER2）阳性是胃癌的一种重要亚型，可为胃癌病理学家提供重要的诊断信息。然而，病理学家通常采用半定量评估方法，在显微镜下将苏木精和伊红（H&E）全切片图像（WSIs）与HER2免疫组化WSIs逐一反复比较，来为胃癌分配HER2评分。这是一个重复、繁琐且主观性很强的过程。此外，WSIs的一幅图像中有数十亿像素，这给计算机辅助诊断（CAD）系统带来了计算挑战。本研究提出了一种用于胃癌HER2定量评估的深度学习算法。与其他使用卷积神经网络提取特征图或对WSIs进行预处理的研究不同，我们在本研究中提出了一种新颖的自动HER2评分框架。为了加速计算过程，我们提出使用重新参数化方案将训练模型与部署模型分离，这显著加快了推理过程。据我们所知，这是第一项为胃癌HER2评分提供深度学习定量算法以辅助病理学家诊断的研究。实验结果证明了我们提出的方法的有效性，HER2评分预测的准确率为0.94。
