VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
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HetScene proposes a two-stage heterogeneous diffusion framework that decomposes scenes into primary structural objects and secondary contextual objects to generate denser, more plausible indoor layouts.
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VitaminP: cross-modal learning enables whole-cell segmentation from routine histology
VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
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HetScene: Heterogeneity-Aware Diffusion for Dense Indoor Scene Generation
HetScene proposes a two-stage heterogeneous diffusion framework that decomposes scenes into primary structural objects and secondary contextual objects to generate denser, more plausible indoor layouts.