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

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.