Abstract：
OBJECTIVE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance. These limitations must be addressed to improve accuracy and practicality.
METHODS: We propose a transformer sequential feature encoding structure to integrate multi-level information from complete CT images, inspired by the clinical practice of using a sequence of cross-sectional slices for diagnosis. We incorporate position encoding and cross-level long-range information fusion modules into the feature extraction CNN network for cross-sectional slices, ensuring high-precision feature extraction.
RESULTS: We conducted comprehensive experiments on a dataset of 124 patients, with respective sizes of 64, 20 and 40 for training, validation and testing. The results of ablation experiments and comparative experiments demonstrated the effectiveness of our approach. Our method outperforms existing state-of-the-art methods in the 3D CT image classification problem of distinguishing between lung infections and pulmonary lymphoma, achieving an accuracy of 0.875, AUC of 0.953 and F1 score of 0.889.
CONCLUSIONS: The experiments verified that our proposed position-enhanced transformer-based sequential feature encoding model is capable of effectively performing high-precision feature extraction and contextual feature fusion in the lungs. It enhances the ability of a standalone CNN network or transformer to extract features, thereby improving the classification performance. The source code is accessible at https://github.com/imchuyu/PTSFE .
METHODS: We propose a transformer sequential feature encoding structure to integrate multi-level information from complete CT images, inspired by the clinical practice of using a sequence of cross-sectional slices for diagnosis. We incorporate position encoding and cross-level long-range information fusion modules into the feature extraction CNN network for cross-sectional slices, ensuring high-precision feature extraction.
RESULTS: We conducted comprehensive experiments on a dataset of 124 patients, with respective sizes of 64, 20 and 40 for training, validation and testing. The results of ablation experiments and comparative experiments demonstrated the effectiveness of our approach. Our method outperforms existing state-of-the-art methods in the 3D CT image classification problem of distinguishing between lung infections and pulmonary lymphoma, achieving an accuracy of 0.875, AUC of 0.953 and F1 score of 0.889.
CONCLUSIONS: The experiments verified that our proposed position-enhanced transformer-based sequential feature encoding model is capable of effectively performing high-precision feature extraction and contextual feature fusion in the lungs. It enhances the ability of a standalone CNN network or transformer to extract features, thereby improving the classification performance. The source code is accessible at https://github.com/imchuyu/PTSFE .
摘要：
目的：使用CT图像区分肺淋巴瘤和肺部感染是具有挑战性的。现有的基于深度神经网络的肺部CT分类模型依赖于2D切片,缺乏全面的信息，需要手动选择。涉及分块的3D模型会损害图像信息并难以降低参数，限制性能。必须解决这些限制以提高准确性和实用性。
方法：我们提出了一种变压器顺序特征编码结构，以集成来自完整CT图像的多级信息，受到使用一系列横截面切片进行诊断的临床实践的启发。我们将位置编码和跨级别远程信息融合模块纳入横截面切片的特征提取CNN网络，确保高精度的特征提取。
结果：我们对124名患者的数据集进行了全面的实验，分别为64、20和40的大小用于训练，验证和测试。消融实验和比较实验的结果证明了我们方法的有效性。我们的方法在区分肺部感染和肺淋巴瘤的3DCT图像分类问题上优于现有的最新方法。准确度为0.875，AUC为0.953，F1评分为0.889。
结论：实验验证了我们提出的基于位置增强变压器的顺序特征编码模型能够有效地在肺部执行高精度特征提取和上下文特征融合。它增强了独立CNN网络或变压器提取特征的能力，从而提高分类性能。源代码可在https://github.com/imchuyu/PTSFE访问。
方法：我们提出了一种变压器顺序特征编码结构，以集成来自完整CT图像的多级信息，受到使用一系列横截面切片进行诊断的临床实践的启发。我们将位置编码和跨级别远程信息融合模块纳入横截面切片的特征提取CNN网络，确保高精度的特征提取。
结果：我们对124名患者的数据集进行了全面的实验，分别为64、20和40的大小用于训练，验证和测试。消融实验和比较实验的结果证明了我们方法的有效性。我们的方法在区分肺部感染和肺淋巴瘤的3DCT图像分类问题上优于现有的最新方法。准确度为0.875，AUC为0.953，F1评分为0.889。
结论：实验验证了我们提出的基于位置增强变压器的顺序特征编码模型能够有效地在肺部执行高精度特征提取和上下文特征融合。它增强了独立CNN网络或变压器提取特征的能力，从而提高分类性能。源代码可在https://github.com/imchuyu/PTSFE访问。
