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

arXiv: https://arxiv.org/abs/2302.11840 · HTML: https://mlsanity.com/paper/2302.11840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 12 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Early Convolutions Help Transformers See Better](https://mlsanity.com/paper/2106.14881/) (2021)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays](https://mlsanity.com/paper/2002.02582/) (2020)
- [Do Lateral Views Help Automated Chest X-ray Predictions?](https://mlsanity.com/paper/1904.08534/) (2019)
- [RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice](https://mlsanity.com/paper/2111.08165/) (2021)

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

- [VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging](https://mlsanity.com/paper/2505.15248/) (2025)

## Abstract (arXiv)

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.