Mr. Robert Siemens Profile

Robert Siemens

at Kingston General Hospital

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 16 March 2020 Presentation + Paper

Towards democratizing AI in MR-based prostate cancer diagnosis: 3.0 to 1.5 Tesla

Andrew Grebenisan, Alireza Sedghi, Alexandre Menard, Jason Izard, Robert Siemens, Parvin Mousavi

Proceedings Volume 11315, 113150R (2020) https://doi.org/10.1117/12.2549413

KEYWORDS: Biopsy, Data modeling, Cancer, Magnetic resonance imaging, Artificial intelligence, Prostate cancer, 3D modeling, Visualization, Image classification

Read Abstract +

In the Western world, nearly 1 in 14 men will be diagnosed with prostate cancer in their lifetimes. The gold standard for detection of PCa is histopathology analysis of biopsy cores taken using trans-rectal ultrasound (TRUS) guidance, a procedure that has a false negative rate of 30 − 45% and serious side effects. Multiparametric MRI (MP-MRI) is quickly becoming part of the standard of care to detect PCa lesions. According to recent multi-centre studies, it has the potential to decrease false positives for PCa detection and reduce the need for biopsy. At the same time, deep learning approaches for aiding radiologists in PCa diagnosis have been on the rise. Most of the solutions in the literature benefit from abundant high-quality data, which limits their translation to clinical settings. For PCa in particular, close to 83% of clinical MRI systems in Canada are 1.5 T, and many centres may not have the high throughput volume of patients required for building locally accurate machine learning models. In this paper, we present preliminary results from a deep learning framework built using publicly available 3.0 T MP-MRI data and re-purposed for 1.5 T clinical data. We achieve areas under the receiver operating curve of up to 0.76 and provide visualization of the most informative areas of the images for the deep models. Our proposed approach has the potential to allow local hospitals to use pre-built AI models fine-tuned for their own cases, taking advantage of externally available large data sets.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years