ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (2017-05-05T00:00:00.000000Z)

TL;DR

A new chest X-rays database, namely ChestX-ray8, is presented, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing, which is validated using the proposed dataset.

Abstract

The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals Picture Archiving and Communication Systems (PACS). On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high precision computer-aided diagnosis (CAD) systems. In this paper, we present a new chest X-ray database, namely ChestX-ray8, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially-located via a unified weakly-supervised multi-label image classification and disease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network based reading chest X-rays (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully-automated high precision CAD systems.

Authors

References58 items

Latent imidazole curing agents by microencapsulation with copolymers

SpineNet: Automatically Pinpointing Classification Evidence in Spinal MRIs

Self-Transfer Learning for Weakly Supervised Lesion Localization

Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities

HeMIS: Hetero-Modal Image Segmentation

WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks

Low-shot visual object recognition

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Learning Deep Features for Discriminative Localization

Deep Residual Learning for Image Recognition

MovieQA: Understanding Stories in Movies through Question-Answering

DenseCap: Fully Convolutional Localization Networks for Dense Captioning

Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources

Order-Embeddings of Images and Language

Visual7W: Grounded Question Answering in Images

Challenges in clinical natural language processing for automated disorder normalization

Preparing a collection of radiology examinations for distribution and retrieval

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Is object localization for free? - Weakly-supervised learning with convolutional neural networks

Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions

Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

U-Net: Convolutional Networks for Biomedical Image Segmentation

Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation

VQA: Visual Question Answering

What Makes for Effective Detection Proposals?

Deep visual-semantic alignments for generating image descriptions

Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.

From image-level to pixel-level labeling with Convolutional Networks

Show and tell: A neural image caption generator

Going deeper with convolutions

Very Deep Convolutional Networks for Large-Scale Image Recognition

ImageNet Large Scale Visual Recognition Challenge

The Pascal Visual Object Classes Challenge: A Retrospective

Caffe: Convolutional Architecture for Fast Feature Embedding

A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations

The benefits of multi-disciplinary research on intervertebral disc degeneration

Microsoft COCO: Common Objects in Context

From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

Selective Search for Object Recognition

ImageNet classification with deep convolutional neural networks

An overview of MetaMap: historical perspective and recent advances

ImageNet: A large-scale hierarchical image database

Et al

Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking

A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries