Whole slide images based cancer survival prediction using multiple instance learning with graph neural networks
Principal Investigator
Name
Weijia Zhang
Degrees
Ph.D.
Institution
Southeast University
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-988
Initial CDAS Request Approval
Dec 5, 2022
Title
Whole slide images based cancer survival prediction using multiple instance learning with graph neural networks
Summary
Our team is working on building models to predict the survival using pathology images. Firstly, we plan to reproduce a paper that uses the dataset. That is "Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks". Our team intends to optimize the capability of networks to improve the prediction performance. The latest multiple instance learning methods can do much better than attention guided method. Besides we will use the latest survival analysis method to try to obtain more accurate survival prediction. As a result, the predictive models would be a personalized treatment recommendation tool which could be used in clinical routines.
In short, NLST pathology image dataset is needed to validate our method performance. Related works would be written as papers to facilitate the development of this field. Thank you!
In short, NLST pathology image dataset is needed to validate our method performance. Related works would be written as papers to facilitate the development of this field. Thank you!
Aims
Use whole silde images and other data to predict patient overall survival
Collaborators
Xin Liu Southeast University