Skip to Main Content

An official website of the United States government

Government Funding Lapse

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted. The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit  cc.nih.gov. Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

Subtype and grade classification using histopathologic slides

Principal Investigator
Name
Gal Aviram
Degrees
BSc
Institution
Curatio - DL
Position Title
CTO
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCOI-924
Initial CDAS Request Approval
Mar 2, 2022
Title
Subtype and grade classification using histopathologic slides
Summary
In this project, we will develop and train a machine learning algorithm using histopathology images collected by the PLCO study.
Our algorithm is expected to automatically find image features that are correlative with the patient's grade and cancer subtype.
Our models are expected to perform predictions of the grade and cancer subtype of the patient based on the characteristic features that were found.
Aims

Aim 1 - Correlate between morphologic features in histopathology images and the patient's cancer grade using ML algorithms.
We will develop a supervised deep convolutional network that will receive slides containing cancer cells as input and will be trained to predict the slide's grade.
Aim 2 - Correlate between morphologic features in histopathology images and the patient's cancer subtype using ML algorithms. Constructing a model for subtype prediction using histopathology slides.

Collaborators

Prof. Kun-Hsing Yu, Harvard University.