关键词:
Lung cancer
摘要:
Adenocarcinoma and squamous cell carcinoma constitute a significant proportion of lung cancer cases, with distinct treatment strategies and prognoses. Accurate differentiation is crucial for clinical treatment. Advanced-stage lung cancer patients often cannot undergo tissue pathology, making cytopathology based on rapid on-site evaluation a feasible approach. However, this method faces challenges such as a shortage of digital pathology experts and diagnostic subjectivity. Current computer-aided digital cytopathology studies often overlook the spatial information between cells. This study proposes a knowledge-fusion cross-attention classification network, utilizing a graph convolutional network branch and a convolutional neural network branch to respectively introduce spatial information knowledge and texture knowledge. The knowledge is fused and classified using a multilayer perceptron, with a cross-attention mechanism proposed for node optimization during the pooling stage. Experiments on a pulmonary cell pathology dataset containing 209 cases showed that the proposed method achieved an accuracy of 0.925 in distinguishing adenocarcinoma from squamous cell carcinoma, with both recall and precision at 0.950, an F1-score of 0.920, and AUC of 0.980. This performance surpasses all comparison methods. In summary, the proposed method can improve the accuracy of diagnosing lung adenocarcinoma and squamous cell carcinoma, providing assistance in rapid intraoperative pathology for lung cancer patients. © Shanghai Jiao Tong University 2024.