Nam-Joon Kim

By jointly optimizing distillation and quantization within a unified teacher-anchored framework, this method ensures consistent training under quantization, suppressing hard negatives and enlarging decision margins, and designs a lightweight cross-attention adapter that enhances non-RGB representations through RGB-guided semantic transfer, narrowing the modality gap.

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.

Video-level labels alone can reliably drive clinical video segmentation: coarse 3D-CAM cues paired with MedSAM2 achieve 94.5% recall and double small-polyp localization without a single manual frame annotation.

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.

Gaze-only models can be significantly improved by injecting lightweight language signals, achieving up to a 2.9 percentage point gain in reading comprehension prediction accuracy.

ProsMAE outperforms traditional methods by leveraging diverse histopathology datasets to significantly improve ISUP grade classification accuracy.

Mamba's linear-complexity SSMs can unify and accelerate dental diagnosis, achieving state-of-the-art results in tooth detection, caries segmentation, anomaly detection, and developmental staging.

Overcoming the challenge of limited and inconsistent imaging criteria for perineural invasion (PNI) diagnosis, NeoNet achieves state-of-the-art prediction accuracy by generating synthetic training data with a 3D Latent Diffusion Model.