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Weiyi Qin
W8Yi
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I built a TCGA WSI feature dataset using UNI2-h. The official release currently has incomplete coverage (see discussion): https://huggingface.co/datasets/MahmoodLab/UNI2-h-features/discussions/2#681b5ed184d0a008fca99297 To make the features easier to use for research, I generated a new dataset: https://huggingface.co/datasets/W8Yi/tcga-wsi-uni2h-features Key differences from the official release: • All detected tissue tiles are encoded (not a sampled subset) • Features can be downloaded per slide instead of large ZIP archives • QC overlay images are provided for visual inspection • UNI2-h 1536-D tile embeddings stored in H5 format • Organized by TCGA project for easier use in MIL / retrieval pipelines Example layout: ``` TCGA-HNSC/ features/*.h5 vis/*__overlay.png ``` Hope this helps others working on computational pathology and TCGA WSI research.
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I built a TCGA WSI feature dataset using UNI2-h. The official release currently has incomplete coverage (see discussion): https://huggingface.co/datasets/MahmoodLab/UNI2-h-features/discussions/2#681b5ed184d0a008fca99297 To make the features easier to use for research, I generated a new dataset: https://huggingface.co/datasets/W8Yi/tcga-wsi-uni2h-features Key differences from the official release: • All detected tissue tiles are encoded (not a sampled subset) • Features can be downloaded per slide instead of large ZIP archives • QC overlay images are provided for visual inspection • UNI2-h 1536-D tile embeddings stored in H5 format • Organized by TCGA project for easier use in MIL / retrieval pipelines Example layout: ``` TCGA-HNSC/ features/*.h5 vis/*__overlay.png ``` Hope this helps others working on computational pathology and TCGA WSI research.
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W8Yi/distilled-wsi-diffusion
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9 days ago