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Samsung Changwon Hospital
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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.
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.
