Comprehensive Genomic Subtyping of Glioma Using Semi-Supervised Multi-Task Deep Learning on Multimodal MRI
High grade glioma (HGG) are the most common, highly infiltrative brain tumors usually with a grim outcome with low survival. Recent comprehensive genomic profiling has greatly elucidated the molecular markers in gliomas that include the mutations in isocitrate dehydrogenase (IDH), 1p/19q co-deletion...
Main Authors: | Priyanka Tupe-Waghmare, Piyush Malpure, Ketan Kotecha, Manish Beniwal, Vani Santosh, Jitender Saini, Madhura Ingalhalikar |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9654221/ |
Similar Items
-
Predictive and discriminative localization of pathology using high resolution class activation maps with CNNs
by: Sumeet Shinde, et al.
Published: (2021-07-01) -
DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping
by: Sana Munquad, et al.
Published: (2023-11-01) -
Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning
by: Diaohan Xiong, et al.
Published: (2022-12-01) -
Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study
by: Vladimir Despotovic, et al.
Published: (2024-03-01) -
Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma
by: Sana Munquad, et al.
Published: (2024-03-01)