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

Abstract

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.

NIPS 2016 Workshop on Machine Learning for Health

References in corpus (3)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Multiple Object Recognition with Visual Attention
• Attention for Fine-Grained Categorization

Cited by in corpus (4)

• Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
• Cosmic Ray Rejection with Attention Augmented Deep Learning
• Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
• Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning