As an important method to diagnose gastric cancer, gastric pathological sections images (GPSI) are hard and time‐consuming to be recognized even by an experienced doctor. An efficient method was designed to detect gastric cancer in magnified (20×) GPSI using deep learning technology. A novel DenseNet architecture was applied, modified with a multistage attention module (MSA‐DenseNet). To develop this model focusing on gastric features, a two‐stage‐input attention module was adopted to select more semantic information of cancer. Moreover, the pretraining process was divided into two steps to improve the effect of the attention mechanism. After training, our method achieved a state‐of‐the‐art performance yielding 0.9947 F1 score and 0.9976 ROC AUC on a test dataset. In line with our expectation in clinical practice, a high recall (0.9929) was produced with high sensitivity to the positive samples. These results indicate that this new model performs better than current artificial detection approaches and its effectiveness is therefore validated in cancer pathological diagnoses.

中文翻译：

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基于多阶段注意-DenseNet的胃病理切片胃癌识别算法

作为诊断胃癌的重要方法，即使有经验的医生也难以识别胃病理切片图像（GPSI）。设计了一种有效的方法来检测放大的胃癌（20 ×）使用深度学习技术的GPSI。应用了新颖的DenseNet架构，并通过多阶段关注模块（MSA-DenseNet）对其进行了修改。为了开发针对胃部特征的模型，采用了两阶段输入注意模块来选择更多的癌症语义信息。此外，预训练过程分为两个步骤，以提高注意力机制的效果。训练后，我们的方法在测试数据集上获得了最先进的性能，产生了0.9947 F1得分和0.9976 ROC AUC。符合我们在临床实践中的期望，产生了对阳性样品具有高敏感性的高召回率（0.9929）。这些结果表明，该新模型的性能优于当前的人工检测方法，因此其有效性在癌症病理诊断中得到了验证。