医学图像中对肺癌亚型的准确识别对肺癌的诊断和治疗具有重要意义。尽管现有方法取得了很大进展，但由于有限的注释数据集、大量的类内差异和高度相似的类间差异，仍然具有挑战性。为解决这些挑战，我们提出了一种频域变换模型（FDTrans），使用TCGA肺癌数据集识别患者肺癌亚型。我们添加了预处理流程，通过基于块的离散余弦变换将组织病理学图像转换到频域，并设计了一个坐标-空间注意力模块（CSAM），通过重新分配不同频率向量的位置信息和通道信息的权重，获取关键细节信息。然后，设计Y、Cb和Cr通道特征的跨域变换块（CDTB），捕获不同组件特征之间的长期依赖和全局上下文连接。同时，对基因组数据进行特征提取以获得特定特征。最后，将图像分支和基因分支融合，并通过全连接层输出分类结果。在10倍交叉验证中，该方法实现了93.16％的AUC和92.33％的整体准确度，优于类似的肺癌亚型分类检测方法。该方法可以帮助医生诊断患者肺癌的亚型分类，并从空间和频域信息中获益。版权所有 © 2023 Elsevier Ltd.

Accurate identification of lung cancer subtypes in medical images is of great significance for the diagnosis and treatment of lung cancer. Despite substantial progress in existing methods, they remain challenging due to limited annotated datasets, large intra-class differences, and high inter-class similarities.To address these challenges, we propose a Frequency Domain Transformer Model (FDTrans) to identify patients' lung cancer subtypes using the TCGA lung cancer dataset. We add a pre-processing process to transfer histopathological images to the frequency domain using a block-based discrete cosine transform and design a coordinate Coordinate-Spatial Attention Module (CSAM) to obtain critical detail information by reassigning weights to the location information and channel information of different frequency vectors. Then, a Cross-Domain Transformer Block (CDTB) is designed for Y, Cb, and Cr channel features, capturing the long-term dependencies and global contextual connections between different component features. At the same time, feature extraction is performed on the genomic data to obtain specific features. Finally, the image branch and the gene branch are fused, and the classification result is output through the fully connected layer.In 10-fold cross-validation, the method achieves an AUC of 93.16% and overall accuracy of 92.33%, which is better than similar current lung cancer subtypes classification detection methods.This method can help physicians diagnose the subtypes classification of lung cancer in patients and can benefit from both spatial and frequency domain information.Copyright © 2023 Elsevier Ltd. All rights reserved.