Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks

04/20/2018
by   Jonathan Rubin, et al.
0

The MIMIC-CXR dataset is (to date) the largest publicly released chest x-ray dataset consisting of 473,064 chest x-rays and 206,574 radiology reports collected from 63,478 patients. We present the results of training and evaluating a collection of deep convolutional neural networks on this dataset to recognize multiple common thorax diseases. To the best of our knowledge, this is the first work that trains CNNs for this task on such a large collection of chest x-ray images, which is over four times the size of the largest previously released chest x-ray corpus. We describe and evaluate individual CNN models trained on frontal and lateral CXR view types. In addition, we present a novel DualNet architecture that emulates routine clinical practice by simultaneously processing both frontal and lateral CXR images obtained from a radiological exam. Our DualNet architecture shows improved performance in recognizing findings in CXR images when compared to applying separate baseline frontal and lateral classifiers.

READ FULL TEXT
research
11/14/2017

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

We develop an algorithm that can detect pneumonia from chest X-rays at a...
research
05/05/2021

Image Embedding and Model Ensembling for Automated Chest X-Ray Interpretation

Chest X-ray (CXR) is perhaps the most frequently-performed radiological ...
research
02/26/2023

MDF-Net: Multimodal Dual-Fusion Network for Abnormality Detection using CXR Images and Clinical Data

This study aims to investigate the effects of including patients' clinic...
research
03/12/2018

Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

Chest X-ray is the most common medical imaging exam used to assess multi...
research
07/16/2018

Longitudinal detection of radiological abnormalities with time-modulated LSTM

Convolutional neural networks (CNNs) have been successfully employed in ...
research
08/14/2021

DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans

X-ray imaging in DICOM format is the most commonly used imaging modality...
research
03/20/2023

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis

While recent advances in large-scale foundational models show promising ...

Please sign up or login with your details

Forgot password? Click here to reset