This study aims to establish a computer-aided diagnosis system for endobronchial ultrasound (EBUS) surgery to assist physicians in the preliminary diagnosis of metastatic cancer. This involves arranging immediate examinations for other sites of metastatic cancer after EBUS surgery, eliminating the need to wait for reports, thereby shortening the waiting time by more than half and enabling patients to detect other cancers earlier, allowing for early planning and implementation of treatment plans. Unlike previous studies on cell image classification, which have abundant datasets for training, this study must also be able to make effective classifications despite the limited amount of case data for lung metastatic cancer. In the realm of small data set classification methods, Few-shot learning (FSL) has become mainstream in recent years. Through its ability to train on small datasets and its strong generalization capabilities, FSL shows potential in this task of lung metastatic cell image classification. This study will adopt the approach of Few-shot learning, referencing existing proposed models, and designing a model architecture for classifying lung metastases cell images. Batch Spectral Regularization (BSR) will be incorporated as a loss update parameter, and the Finetune method of PMF will be modified. In terms of test results, the addition of BSR and the modified Finetune method further increases the accuracy by 8.89% to 65.60%, outperforming other FSL methods. This study confirms that FSL is superior to supervised and transfer learning in classifying metastatic cancer and demonstrates that using BSR as a loss function and modifying Finetune can enhance the model's capabilities.

建立一种计算机辅助诊断系统，用于支持医生在内支气管超声（EBUS）手术中进行转移癌的初步诊断，通过安排EBUS手术后其他部位的立即检查，消除了等待报告的需要，将等待时间减半以上，并使患者能够更早地发现其他癌症，从而实现早期治疗计划的早期规划和实施。同时，通过引用现有的建议模型和设计模型架构来分类肺转移细胞图像，本研究证实了少样本学习在转移癌分类中的优越性，并表明使用Batch Spectral Regularization（BSR）作为损失函数和修改Finetune能增强该模型的能力。

Using Few-Shot Learning to Classify Primary Lung Cancer and Other
  Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial
  Ultrasound Procedures

利用少样本学习通过内镜超声程序对细胞学影像进行初级肺癌和其他恶性肿瘤的分类