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Hankuk University of Foreign Studies
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CSWinUNETR achieves unprecedented segmentation accuracy for thin anatomical structures, outperforming existing methods without complex post-processing.
Adaptive Binning transforms how we leverage unlabeled medical tabular data, achieving significant performance gains without the need for costly expert labels.
OTCHA's optimal transport-driven approach not only refines token representations but also filters out irrelevant information, leading to superior multi-view classification performance.
Ditch the PET scan? A new Vision Transformer, CSV-ViT, uses MRI data and a novel surface tokenization method to predict Alzheimer's Disease pathologies with higher accuracy than existing surface-based models.
Synthesizing realistic, time-specific brain MRIs that reflect individual Alzheimer's progression is now possible by conditioning a diffusion transformer on detailed clinical text prompts.
