An automated approach for annotating Gleason patterns in whole-mount prostate cancer histology using deep learning

Michael Brehler; Allison Lowman; Samuel Bobholz; Savannah Duenweg; Fitzgerald Kyereme; Cassandra Naze; John Sherman; Kenneth Iczkowski; Peter S. LaViolette

doi:10.1117/12.2610793

4 April 2022 An automated approach for annotating Gleason patterns in whole-mount prostate cancer histology using deep learning

Michael Brehler, Allison Lowman, Samuel Bobholz, Savannah Duenweg, Fitzgerald Kyereme, Cassandra Naze, John Sherman, Kenneth Iczkowski, Peter S. LaViolette

Author Affiliations +

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391L (2022) https://doi.org/10.1117/12.2610793
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

There is no online version at this time. The PDF is only available to people who have bought the paper or have a subscription.

Citation Download Citation

Michael Brehler, Allison Lowman, Samuel Bobholz, Savannah Duenweg, Fitzgerald Kyereme, Cassandra Naze, John Sherman, Kenneth Iczkowski, and Peter S. LaViolette "An automated approach for annotating Gleason patterns in whole-mount prostate cancer histology using deep learning", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391L (4 April 2022); https://doi.org/10.1117/12.2610793

ACCESS THE FULL ARTICLE

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