# CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans

arXiv: https://arxiv.org/abs/2110.08721 · HTML: https://mlsanity.com/paper/2110.08721/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 17 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models](https://mlsanity.com/paper/2107.01682/) (2021)
- [COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans](https://mlsanity.com/paper/2010.16041/) (2020)
- [Visual Transformer with Statistical Test for COVID-19 Classification](https://mlsanity.com/paper/2107.05334/) (2021)
- [CT-CAPS: Feature Extraction-based Automated Framework for COVID-19 Disease Identification from Chest CT Scans using Capsule Networks](https://mlsanity.com/paper/2010.16043/) (2020)
- [Human-level COVID-19 Diagnosis from Low-dose CT Scans Using a Two-stage Time-distributed Capsule Network](https://mlsanity.com/paper/2105.14656/) (2021)

## Built on by (most cited first, 1 of 1)

- [PneumoLLM: Harnessing the Power of Large Language Model for Pneumoconiosis Diagnosis](https://mlsanity.com/paper/2312.03490/) (2023)

## Abstract (arXiv)

Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which Lung Adenocarcinoma (LUAC) has recently been the most prevalent one. The current approach to determine the invasiveness of LUACs is surgical resection, which is not a viable solution to fight lung cancer in a timely fashion. An alternative approach is to analyze chest Computed Tomography (CT) scans. The radiologists' analysis based on CT images, however, is subjective and might result in a low accuracy. In this paper, a transformer-based framework, referred to as the "CAE-Transformer", is developed to efficiently classify LUACs using whole CT images instead of finely annotated nodules. The proposed CAE-Transformer can achieve high accuracy over a small dataset and requires minor supervision from radiologists. The CAE Transformer utilizes an encoder to automatically extract informative features from CT slices, which are then fed to a modified transformer to capture global inter-slice relations and provide classification labels. Experimental results on our in-house dataset of 114 pathologically proven Sub-Solid Nodules (SSNs) demonstrate the superiority of the CAE-Transformer over its counterparts, achieving an accuracy of 87.73%, sensitivity of 88.67%, specificity of 86.33%, and AUC of 0.913, using a 10-fold cross-validation.