  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}).
