# Learning what to look in chest X-rays with a recurrent visual attention model

arXiv: https://arxiv.org/abs/1701.06452 · HTML: https://mlsanity.com/paper/1701.06452/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation](https://mlsanity.com/paper/2104.10326/) (2021)
- [Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning](https://mlsanity.com/paper/2101.08992/) (2021)
- [BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognition](https://mlsanity.com/paper/2302.11082/) (2023)

## Abstract (arXiv)

X-rays are commonly performed imaging tests that use small amounts of radiation to produce pictures of the organs, tissues, and bones of the body. X-rays of the chest are used to detect abnormalities or diseases of the airways, blood vessels, bones, heart, and lungs. In this work we present a stochastic attention-based model that is capable of learning what regions within a chest X-ray scan should be visually explored in order to conclude that the scan contains a specific radiological abnormality. The proposed model is a recurrent neural network (RNN) that learns to sequentially sample the entire X-ray and focus only on informative areas that are likely to contain the relevant information. We report on experiments carried out with more than 100,000 X-rays containing enlarged hearts or medical devices. The model has been trained using reinforcement learning methods to learn task-specific policies.