Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI
Background In the field of glioma, transcriptome subtypes have been considered as an
important diagnostic and prognostic biomarker that may help improve the treatment efficacy …
important diagnostic and prognostic biomarker that may help improve the treatment efficacy …
Current status and quality of machine learning-based radiomics studies for glioma grading: a systematic review
Introduction: Radiomics now has significant momentum in the era of precision medicine.
Glioma is one of the pathologies that has been extensively evaluated by radiomics …
Glioma is one of the pathologies that has been extensively evaluated by radiomics …
Multimodal disentangled variational autoencoder with game theoretic interpretability for glioma grading
Effective fusion of multimodal magnetic resonance imaging (MRI) is of great significance to
boost the accuracy of glioma grading thanks to the complementary information provided by …
boost the accuracy of glioma grading thanks to the complementary information provided by …
Observing deep radiomics for the classification of glioma grades
K Kobayashi, M Miyake, M Takahashi, R Hamamoto - Scientific Reports, 2021 - nature.com
Deep learning is a promising method for medical image analysis because it can
automatically acquire meaningful representations from raw data. However, a technical …
automatically acquire meaningful representations from raw data. However, a technical …
뇌교종환자의쌍이없는T1-T2 자기공명영상변환모델
정승완， 박현진 - 대한전자공학회학술대회, 2021 - dbpia.co.kr
Glioma is a brain tumor that occurs in the neuroglia cell in the brain. Glioma is diagnosed
using multimodal magnetic resonance imaging (MRI). However, modalities can often be …
using multimodal magnetic resonance imaging (MRI). However, modalities can often be …