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Detection and classification of abnormalities in Chest X-Rays

Principal Investigator
Name
Robert Rowley
Degrees
M.D.
Institution
Flow Health, Inc.
Position Title
Chief Medical Officer
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-296
Initial CDAS Request Approval
Sep 5, 2017
Title
Detection and classification of abnormalities in Chest X-Rays
Summary
We propose using deep learning methods to build algorithms for classification of chest X-rays (CXRs) in the diseases/findings available in the PLCO dataset. More specifically we are planning to use deep convolutional neural networks (CNNs) and other deep learning architectures to help understand and identify normal and abnormal appearances of diseases in CXRs.
Aims

Aim 1: We are planning to build classifiers using CNNs to help classify CXRs. We believe that because PLCO dataset offers a large number of CXRs along with annotations the CNNs will be able to be trained and learn various abnormalities. More specifically we are planning to build:
1) a classifier for normal versus abnormal (cancer) CXRs
2) a classifier or classifiers for normal versus other diseases/conditions found in the PLCO dataset (e.g. nodules, emphysema, etc)
Aim 2: Using radiologists' input we are planning to annotate CXRs. Using trained classifiers as well as radiologists' ground truths the classifiers from Aim 1 will be able to improve and provide the location of the diseases/conditions.
Aim 3: We are planning to enhance the classifiers of Aim 1 and Aim 2 by incorporating metadata (i.e. patients' non-imaging information: age, gender, history, etc.). We believe that this additional data will provide insightful information and potential correlations.

Collaborators

Alex Meshkin - CEO
Dmitry Shingarev - Machine Learning Director