Noise Robustness

Cross-source consensus on Noise Robustness from 1 sources and 5 claims.

1 sources · 5 claims

How it works

Image-level Gaussian noise on T1Gd moderately reduced patient accuracy from 0.67 to 0.61 while AUC was 0.67. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
Gate-level noise reduced patient accuracy to 0.51 and AUC to 0.56. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
Hybrid image-and-gate noise improved performance beyond the baseline in validation accuracy, patient accuracy, and AUC. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
Gaussian perturbation was used as a simpler approximation because built-in Cirq noise channels caused long simulations and sometimes kernel failures. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
The article interprets the hybrid noise result as stochastic regularization similar to dropout. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma