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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.
