Model Performance
Cross-source consensus on Model Performance from 1 sources and 5 claims.
1 sources · 5 claims
Comparisons
Evidence quality
Highlighted claims
- The baseline T1Gd IA-QCNN achieved patient-level test accuracy of 0.67 and AUC of 0.66. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
- The mpMRI IA-QCNN achieved patient-level test accuracy of 0.49 and AUC of 0.45. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
- T1Gd IA-QCNN patient accuracy exceeded the DNN and VGG16 comparisons reported in the article. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
- Compared with T1Gd, mpMRI increased training accuracy but reduced validation accuracy and patient-level discrimination. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
- Transfer learning and contemporary deep models generally underperformed T1Gd IA-QCNN at patient level. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma