Post
2764
I built a TCGA WSI feature dataset using UNI2-h.
The official release currently has incomplete coverage (see discussion):
MahmoodLab/UNI2-h-features#2
To make the features easier to use for research, I generated a new dataset:
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:
Hope this helps others working on computational pathology and TCGA WSI research.
The official release currently has incomplete coverage (see discussion):
MahmoodLab/UNI2-h-features#2
To make the features easier to use for research, I generated a new dataset:
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.pngHope this helps others working on computational pathology and TCGA WSI research.
