Medical College, Guizhou University, Guizhou 550000, China; Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guizhou 550002, China.
College of Computer Science and Technology, Guizhou University, Guizhou 550025, China.
Comput Methods Programs Biomed. 2022 Jun;221:106924. doi: 10.1016/j.cmpb.2022.106924. Epub 2022 May 29.
Gastric cancer has high morbidity and mortality compared to other cancers. Accurate histopathological diagnosis has great significance for the treatment of gastric cancer. With the development of artificial intelligence, many researchers have applied deep learning for the classification of gastric cancer pathological images. However, most studies have used binary classification on pathological images of gastric cancer, which is insufficient with respect to the clinical requirements. Therefore, we proposed a multi-classification method based on deep learning with more practical clinical value.
In this study, we developed a novel multi-scale model called StoHisNet based on Transformer and the convolutional neural network (CNN) for the multi-classification task. StoHisNet adopts Transformer to learn global features to alleviate the inherent limitations of the convolution operation. The proposed StoHisNet can classify the publicly available pathological images of a gastric dataset into four categories -normal tissue, tubular adenocarcinoma, mucinous adenocarcinoma, and papillary adenocarcinoma.
The accuracy, F1-score, recall, and precision of the proposed model in the public gastric pathological image dataset were 94.69%, 94.96%, 94.95%, and 94.97%, respectively. We conducted additional experiments using two other public datasets to verify the generalization ability of the model. On the BreakHis dataset, our model performed better compared with other classification models, and the accuracy was 91.64%. Similarly, on the four-classification task on the Endometrium dataset, our model showed better classification ability than others with accuracy of 81.74%. These experiments showed that the proposed model has excellent ability of classification and generalization.
The StoHisNet model had high performance in the multi-classification on gastric histopathological images and showed strong generalization ability on other pathological datasets. This model may be a potential tool to assist pathologists in the analysis of gastric histopathological images.
与其他癌症相比，胃癌的发病率和死亡率都较高。准确的组织病理学诊断对胃癌的治疗具有重要意义。随着人工智能的发展，许多研究人员已经将深度学习应用于胃癌病理图像的分类。然而，大多数研究仅对胃癌病理图像进行了二进制分类，这在临床需求方面是不够的。因此，我们提出了一种基于深度学习的多分类方法，具有更实际的临床价值。
在本研究中，我们开发了一种名为 StoHisNet 的新型多尺度模型，该模型基于 Transformer 和卷积神经网络（CNN），用于多分类任务。StoHisNet 采用 Transformer 来学习全局特征，以缓解卷积运算的固有局限性。所提出的 StoHisNet 可以将公开的胃病理图像数据集分为四个类别-正常组织、管状腺癌、黏液腺癌和乳头状腺癌。
所提出的模型在公开的胃病理图像数据集上的准确性、F1 分数、召回率和精度分别为 94.69%、94.96%、94.95%和 94.97%。我们还使用另外两个公共数据集进行了额外的实验，以验证模型的泛化能力。在 BreakHis 数据集上，我们的模型与其他分类模型相比表现更好，准确性为 91.64%。同样，在 Endometrium 数据集的四分类任务上，我们的模型表现出比其他模型更好的分类能力，准确率为 81.74%。这些实验表明，所提出的模型具有出色的分类和泛化能力。
StoHisNet 模型在胃组织病理学图像的多分类中具有出色的性能，并且在其他病理数据集上具有强大的泛化能力。该模型可能是辅助病理学家分析胃组织病理学图像的潜在工具。
