# Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer

arXiv: https://arxiv.org/abs/2205.15290 · HTML: https://mlsanity.com/paper/2205.15290/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Zero-Shot Learning Through Cross-Modal Transfer](https://mlsanity.com/paper/1301.3666/) (2013)
- [Lung and Colon Cancer Histopathological Image Dataset (LC25000)](https://mlsanity.com/paper/1912.12142/) (2019)
- [Image biomarker standardisation initiative](https://mlsanity.com/paper/1612.07003/) (2016)
- [Revisiting the Transferability of Supervised Pretraining: an MLP Perspective](https://mlsanity.com/paper/2112.00496/) (2021)
- [Computational Pathology: Challenges and Promises for Tissue Analysis](https://mlsanity.com/paper/1601.00027/) (2015)

## Built on by (most cited first, 2 of 2)

- [Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines](https://mlsanity.com/paper/2305.00109/) (2023)
- [Dual-channel Prototype Network for few-shot Classification of Pathological Images](https://mlsanity.com/paper/2311.07871/) (2023)

## Abstract (arXiv)

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).