Multi-Modal Learning for Predicting the Genotype of Glioma-Reference-Cited by-同舟云学术

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Multi-Modal Learning for Predicting the Genotype of Glioma

Published:2023-11 Issue:11 Volume:42 Page:3167-3178
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Wei Yiran¹^ORCID,Chen Xi²^ORCID,Zhu Lei³^ORCID,Zhang Lipei⁴,Schönlieb Carola-Bibiane⁴^ORCID,Price Stephen¹^ORCID,Li Chao¹^ORCID

Affiliation:

1. Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge, U.K

2. Department of Computer Science, University of Bath, Bath, U.K

3. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

4. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K

Funder

National Institute for Health Research (NIHR)

National Institute for Health Research (NIHR), Career Development Fellowship

Guarantors of Brain Fellowship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10299534/10042035.pdf?arnumber=10042035

Reference45 articles.

1. Predictive and discriminative localization of IDH genotype in high grade gliomas using deep convolutional neural nets

2. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

3. Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging

4. Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study

5. Brain Graphs: Graphical Models of the Human Brain Connectome

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