Compact deep models are attractive for medical image classification, but conventional knowledge distillation mainly transfers class-level predictions and may not adequately preserve lesion-relevant spatial cues. To address this limitation, we propose LaHKD, a lesion-aware hierarchical knowledge distillation framework f...
Ya-Rong Liu, Run-Mei Xie, Xiao-Lan Xie et al.· Journal of Imaging· 0 citations
Short-term electric load forecasting is essential to the secure and stable operation and economic dispatch of power systems, and its accuracy directly affects grid dispatch decisions and operational efficiency. To address the inadequate modeling of heterogeneity among historical exogenous variables, the underutilizatio...
The proposed MST-iTransformer model achieves simultaneous reductions in prediction errors and inference latency, validating the effectiveness of the multi-scale temporal encoding, sparse attention mechanism, and adaptive feature selection modules in improving forecasting accuracy and computational efficiency.