Image processing algorithm to screen for lung cancer on radiology images
Principal Investigator
Name
Alexandros Karargyris
Degrees
PhD
Institution
IBM
Position Title
Software Engineering Researcher
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-214
Initial CDAS Request Approval
Jun 15, 2016
Title
Image processing algorithm to screen for lung cancer on radiology images
Summary
Medical Sieve is an ambitious long-term exploratory grand challenge project to build a next generation cognitive assistant with advanced multimodal analytics, clinical knowledge and reasoning capabilities that is qualified to assist in clinical decision making in radiology and cardiology. The project aims at producing a sieve that filters essential clinical and diagnostic imaging information to form anomaly-driven summaries and recommendations that tremendously reduce the viewing load of clinicians without negatively impacting diagnosis. More information about the project can be found here: http://researcher.watson.ibm.com/researcher/view_group.php?id=4384
Aims
1) To investigate whether hardcrafted image features can help with CXR image classification as normal versus abnormal in terms of lung disease (e.g. lung cancer). To provide accuracy results.
2) To investigate whether deep learning approaches can help with CXR image classification as normal versus abnormal in terms of lung disease (e.g. lung cancer). To provide accuracy results.
3) To investigate whether patient demographics and metadata from patients' records coupled with aforementioned image features can improve overall classification accuracy.
4) To investigate whether aforementioned image features can help with disease progression prediction.
Collaborators
Tanveer Syeda-Mahmood
Hongzhi Wang
Ehsan Dehgan Marvast
Tyler Baldwin