Episodic sampling for class-balanced batches in low-data CT segmentation delays overfitting compared to random or weighted sampling, revealing training iteration budget as a key evaluation confound.
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2026 2representative citing papers
The MHHTOF framework uses momentum-constrained heuristic optimization and residual DRL to achieve faster convergence, lower stable costs, and safer velocity profiles than baselines in visually impaired navigation scenarios.
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Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation
Episodic sampling for class-balanced batches in low-data CT segmentation delays overfitting compared to random or weighted sampling, revealing training iteration budget as a key evaluation confound.
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Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
The MHHTOF framework uses momentum-constrained heuristic optimization and residual DRL to achieve faster convergence, lower stable costs, and safer velocity profiles than baselines in visually impaired navigation scenarios.