Abstract：
Lung cancer has seriously threatened human health due to its high lethality and morbidity. Lung adenocarcinoma, in particular, is one of the most common subtypes of lung cancer. Pathological diagnosis is regarded as the gold standard for cancer diagnosis. However, the traditional manual screening of lung cancer pathology images is time consuming and error prone. Computer-aided diagnostic systems have emerged to solve this problem. Current research methods are unable to fully exploit the beneficial features inherent within patches, and they are characterized by high model complexity and significant computational effort. In this study, a deep learning framework called Multi-Scale Network (MSNet) is proposed for the automatic detection of lung adenocarcinoma pathology images. MSNet is designed to efficiently harness the valuable features within data patches, while simultaneously reducing model complexity, computational demands, and storage space requirements. The MSNet framework employs a dual data stream input method. In this input method, MSNet combines Swin Transformer and MLP-Mixer models to address global information between patches and the local information within each patch. Subsequently, MSNet uses the Multilayer Perceptron (MLP) module to fuse local and global features and perform classification to output the final detection results. In addition, a dataset of lung adenocarcinoma pathology images containing three categories is created for training and testing the MSNet framework. Experimental results show that the diagnostic accuracy of MSNet for lung adenocarcinoma pathology images is 96.55 %. In summary, MSNet has high classification performance and shows effectiveness and potential in the classification of lung adenocarcinoma pathology images.
摘要：
肺癌以其高致死率和高发病率严重威胁人类健康。肺腺癌，特别是,是肺癌最常见的亚型之一。病理诊断被视为癌症诊断的金标准。然而,传统的人工筛查肺癌病理图像耗时且容易出错。计算机辅助诊断系统已经出现来解决这个问题。当前的研究方法无法充分利用补丁固有的有益特征，它们的特点是模型复杂度高，计算量大。在这项研究中,提出了一种称为多尺度网络（MSNet）的深度学习框架，用于自动检测肺腺癌病理图像。MSNet旨在有效地利用数据补丁中的重要功能，在降低模型复杂性的同时，计算需求，和存储空间的要求。MSNet框架采用双数据流输入方法。在此输入法中，MSNet结合了SwinTransformer和MLP-Mixer模型，以解决补丁之间的全局信息以及每个补丁中的本地信息。随后，MSNet使用多层感知器（MLP）模块融合局部和全局特征并执行分类以输出最终检测结果。此外,创建包含三个类别的肺腺癌病理图像的数据集以用于训练和测试MSNet框架。实验结果表明,MSNet对肺腺癌病理图像的诊断准确率为96.55%。总之,MSNet具有较高的分类性能，在肺腺癌病理图像分类中显示出有效性和潜力。
