DEEP LEARNING-BASED INTEGRATION OF HISTOLOGY AND RADIOLOGY FOR IMPROVED SURVIVAL OUTCOME PREDICTION IN GLIOMA PATIENTS
Main Authors: | Luoting Zhuang, Jana Lipkova, Richard Chen, Faisal Mahmood |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2022-01-01
|
Series: | Journal of Pathology Informatics |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353922000608 |
Similar Items
-
PAN-CANCER INTEGRATIVE HISTOLOGY-GENOMIC ANALYSIS VIA INTERPRETABLE MULTIMODAL DEEP LEARNING
by: Richard J. Chen, et al.
Published: (2022-01-01) -
Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images Using Machine Learning Ensemble Regression Methods
by: Faisal Altaf Rathore, et al.
Published: (2022-10-01) -
Integrating Radiosensitivity Gene Signature Improves Glioma Outcome and Radiotherapy Response Prediction
by: Shan Wu, et al.
Published: (2022-09-01) -
Quantitative integration of radiomic and genomic data improves survival prediction of low-grade glioma patients
by: Chen Ma, et al.
Published: (2021-04-01) -
Automatic treatment outcome prediction with DeepInteg based on multimodal radiological images in rectal cancer
by: Yihuang Hu, et al.
Published: (2023-02-01)