# Endoscopy Classification Model Using Swin Transformer and Saliency Map

arXiv: https://arxiv.org/abs/2303.06736 · HTML: https://mlsanity.com/paper/2303.06736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 12 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Localization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks](https://mlsanity.com/paper/1911.00908/) (2019)
- [Gastrointestinal Disorder Detection with a Transformer Based Approach](https://mlsanity.com/paper/2210.03168/) (2022)
- [Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context](https://mlsanity.com/paper/2205.09678/) (2022)

## Abstract (arXiv)

Endoscopy is a valuable tool for the early diagnosis of colon cancer. However, it requires the expertise of endoscopists and is a time-consuming process. In this work, we propose a new multi-label classification method, which considers two aspects of learning approaches (local and global views) for endoscopic image classification. The model consists of a Swin transformer branch and a modified VGG16 model as a CNN branch. To help the learning process of the CNN branch, the model employs saliency maps and endoscopy images and concatenates them. The results demonstrate that this method performed well for endoscopic medical images by utilizing local and global features of the images. Furthermore, quantitative evaluations prove the proposed method's superiority over state-of-the-art works.