# Improved EATFormer: A Vision Transformer for Medical Image Classification

arXiv: https://arxiv.org/abs/2403.13167 · HTML: https://mlsanity.com/paper/2403.13167/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Vision Transformer with Deformable Attention](https://mlsanity.com/paper/2201.00520/) (2022)
- [LocalViT: Analyzing Locality in Vision Transformers](https://mlsanity.com/paper/2104.05707/) (2021)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [MedViT: A Robust Vision Transformer for Generalized Medical Image Classification](https://mlsanity.com/paper/2302.09462/) (2023)
- [EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm](https://mlsanity.com/paper/2206.09325/) (2022)
- [BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation](https://mlsanity.com/paper/2402.08793/) (2024)

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

- [Systematic Integration of Attention Modules into CNNs for Accurate and Generalizable Medical Image Diagnosis](https://mlsanity.com/paper/2509.05343/) (2025)

## Abstract (arXiv)

The accurate analysis of medical images is vital for diagnosing and predicting medical conditions. Traditional approaches relying on radiologists and clinicians suffer from inconsistencies and missed diagnoses. Computer-aided diagnosis systems can assist in achieving early, accurate, and efficient diagnoses. This paper presents an improved Evolutionary Algorithm-based Transformer architecture for medical image classification using Vision Transformers. The proposed EATFormer architecture combines the strengths of Convolutional Neural Networks and Vision Transformers, leveraging their ability to identify patterns in data and adapt to specific characteristics. The architecture incorporates novel components, including the Enhanced EA-based Transformer block with Feed-Forward Network, Global and Local Interaction , and Multi-Scale Region Aggregation modules. It also introduces the Modulated Deformable MSA module for dynamic modeling of irregular locations. The paper discusses the Vision Transformer (ViT) model's key features, such as patch-based processing, positional context incorporation, and Multi-Head Attention mechanism. It introduces the Multi-Scale Region Aggregation module, which aggregates information from different receptive fields to provide an inductive bias. The Global and Local Interaction module enhances the MSA-based global module by introducing a local path for extracting discriminative local information. Experimental results on the Chest X-ray and Kvasir datasets demonstrate that the proposed EATFormer significantly improves prediction speed and accuracy compared to baseline models.