Energy-Based Slice Selection
Cross-source consensus on Energy-Based Slice Selection from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Dosage & preparation
Preparation
Comparisons
Highlighted claims
- The top ten slices by energy score are selected for each patient. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
- The slice energy score is computed as mean pixel intensity multiplied by pixel-intensity standard deviation. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
- Each normalized T1Gd slice is resized to 64 by 64 before computing its energy score. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
- Patient labels are repeated ten times to align with the selected slice-level labels. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
- Energy-based slice selection is used instead of central-slice heuristics, all-slice voting, sequence modeling, segmentation, or manual annotation. — A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma