# Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review

arXiv: https://arxiv.org/abs/2309.02783 · HTML: https://mlsanity.com/paper/2309.02783/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 66 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [A graph-transformer for whole slide image classification](https://mlsanity.com/paper/2205.09671/) (2022)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)](https://mlsanity.com/paper/2205.10342/) (2022)
- [COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models](https://mlsanity.com/paper/2107.01682/) (2021)
- [Generative Adversarial Network for Medical Images (MI-GAN)](https://mlsanity.com/paper/1810.00551/) (2018)
- [T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging](https://mlsanity.com/paper/2111.07535/) (2021)
- [Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection](https://mlsanity.com/paper/2205.00122/) (2022)
- [Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics](https://mlsanity.com/paper/2211.16632/) (2022)
- [Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers](https://mlsanity.com/paper/2004.05640/) (2020)
- [AMMASurv: Asymmetrical Multi-Modal Attention for Accurate Survival Analysis with Whole Slide Images and Gene Expression Data](https://mlsanity.com/paper/2108.12565/) (2021)
- [Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans](https://mlsanity.com/paper/2206.03049/) (2022)

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

- [From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research](https://mlsanity.com/paper/2507.09028/) (2025)
- [A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification](https://mlsanity.com/paper/2501.18294/) (2025)

## Abstract (arXiv)

Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/.