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SCINTILLA-SNN, a 3D spiking network composed of a four-stage hierarchical backbone and a Multi-Scale Spike Aggregation (MSSA) module for PNI prediction, is proposed, which achieves an AUROC of 0.748 under 5-fold cross-validation, while reducing the estimated inference energy by 23.18$\times compared with dense MAC-only computation of the same network.
Results suggest that axial order provides a useful inductive bias for weakly supervised PNI prediction from MRI, suggesting that axial order provides a useful inductive bias for weakly supervised PNI prediction from MRI.
MMA-Former's innovative attention mechanism enables spatially adaptive feature extraction, significantly improving PNI prediction accuracy from 3D MRI scans.
LoSA-Net outperforms existing models in predicting perineural invasion by effectively preserving crucial boundary details in 3D MRI scans.
Adaptive routing in transformer-based models can drastically enhance the efficiency of PNI prediction while maintaining high accuracy in capturing subtle imaging features.
