论文标题
使用视觉变压器的肺癌多标签分类的零射击和少量学习
Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer
论文作者
论文摘要
肺癌是全球与癌症相关死亡的主要原因。肺腺癌（LUAD）和肺鳞状细胞癌（LUSC）是非小细胞肺癌（NSCLC）最常见的组织学亚型。组织学是肺癌诊断的重要工具。病理学家根据主要亚型进行分类。尽管形态仍然是诊断的标准，但需要开发重要的工具来阐明诊断。在我们的研究中，我们利用预先训练的视觉变压器（VIT）模型在组织学切片（从数据集LC25000）上分类了多个标签肺癌，零弹药和少量设置。然后，我们比较了零拍的性能和几乎没有弹药的VIT在准确性，精度，回忆，灵敏度和特异性上。我们的研究表明，预先训练的VIT模型在零拍设置中具有良好的性能，竞争精确度（$ 99.87 \％$ $）在几次设置（{epoch = 1}）和最佳结果（$ 100.00 \％\％\％\％$ $在验证集和测试集中），在几次射击Seeting中（{epoch = 5}）。
Lung cancer is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common histologic subtypes of non-small-cell lung cancer (NSCLC). Histology is an essential tool for lung cancer diagnosis. Pathologists make classifications according to the dominant subtypes. Although morphology remains the standard for diagnosis, significant tool needs to be developed to elucidate the diagnosis. In our study, we utilize the pre-trained Vision Transformer (ViT) model to classify multiple label lung cancer on histologic slices (from dataset LC25000), in both Zero-Shot and Few-Shot settings. Then we compare the performance of Zero-Shot and Few-Shot ViT on accuracy, precision, recall, sensitivity and specificity. Our study show that the pre-trained ViT model has a good performance in Zero-Shot setting, a competitive accuracy ($99.87\%$) in Few-Shot setting ({epoch = 1}) and an optimal result ($100.00\%$ on both validation set and test set) in Few-Shot seeting ({epoch = 5}).
