Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures
arxiv(2024)
摘要
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
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.
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