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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.
Medical imaging AI can now leverage a self-supervised pre-training method that understands 3D context, boosting reconstruction quality beyond what's possible with 2D-centric approaches.
Fine-tuning only the decoder of a trajectory prediction model pretrained on US data slashes prediction error by 66% when adapting to Korean driving environments.
Synthetic MRI data, generated by a segmentation-conditioned diffusion model, can measurably improve the performance of 3D U-Nets for hepatic segmentation.
