# StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray images

## TL;DR

This paper introduces StudyFormer, an attention-based multi-view classifier utilizing Vision Transformers to enhance multi-label X-ray image classification by effectively combining information from multiple views, outperforming existing models.

## Contribution

The paper presents a novel multi-view classification model that integrates CNNs with Vision Transformers for improved X-ray image analysis, demonstrating superior performance over traditional methods.

## Key findings

- Outperforms single-view models in accuracy
- Effective multi-label classification on 41 labels
- Demonstrates scalability on large dataset of 363,000 images

## Abstract

Chest X-ray images are commonly used in medical diagnosis, and AI models have been developed to assist with the interpretation of these images. However, many of these models rely on information from a single view of the X-ray, while multiple views may be available. In this work, we propose a novel approach for combining information from multiple views to improve the performance of X-ray image classification. Our approach is based on the use of a convolutional neural network to extract feature maps from each view, followed by an attention mechanism implemented using a Vision Transformer. The resulting model is able to perform multi-label classification on 41 labels and outperforms both single-view models and traditional multi-view classification architectures. We demonstrate the effectiveness of our approach through experiments on a dataset of 363,000 X-ray images.