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.

翻译：本研究旨在建立一种用于支气管内超声（EBUS）手术的计算机辅助诊断系统，以协助医生对转移性癌症进行初步诊断。该系统可在EBUS手术后立即安排对其他转移性癌症部位的检查，无需等待报告，从而将等待时间缩短一半以上，使患者能够更早发现其他癌症，从而尽早规划并实施治疗方案。与以往拥有丰富训练数据集的细胞图像分类研究不同，本研究需要在肺转移性癌症病例数据有限的情况下仍能进行有效分类。在小样本数据集分类方法领域，小样本学习（FSL）近年来已成为主流方法。通过在小数据集上的训练能力及其强大的泛化性能，FSL在肺转移性细胞图像分类任务中展现出潜力。本研究将采用小样本学习的方法，参考现有已提出的模型，设计用于肺转移性细胞图像分类的模型架构。将引入批量谱正则化（BSR）作为损失更新参数，并改进PMF的微调方法。在测试结果方面，加入BSR和改进的微调方法使准确率进一步提升8.89%，达到65.60%，优于其他小样本学习方法。本研究证实了FSL在转移性癌症分类中优于监督学习和迁移学习，并证明使用BSR作为损失函数以及改进微调方法能够增强模型能力。