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Multi-model dataset for joint histology and omics inference. Wraps a single WSI together with optional bulk RNA data, providing lazy patch access and a PyTorch-compatible __getitem__ interface.

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MultiModelData

class MultiModelData()
Wraps a single Whole Slide Image with its extraction plan. On construction the slide is opened via WSI and the extractor is fitted + executed to produce a list of RegionSpec. Each spec describes one tile/patch location. Patches are read lazily via get_patch.
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wsi_path
Path to the WSI file (any format supported by WSI).
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bulk_rna_path
Optional path to bulk RNA CSV.
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extractor
A configured TileExtractor. A fresh fit_extract is called for every slide so the same extractor object can be reused across slides.
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transform
Optional callable applied to the raw np.ndarray patch before it is returned. Receives (H, W, C) uint8 and should return a transformed array (or tensor).
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path
Path
Resolved slide path.
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bulk_rna_path
Path | None
Resolved path to bulk RNA CSV.
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reader
WSIReader
Open reader for the slide.
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specs
List[RegionSpec]
Extraction plan (one entry per patch).
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slide_name
str
Stem of the slide filename.
Example: 
wsi = MultiModelData("tissue.svs", extractor)
patch, meta = wsi.get_patch(0)

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get_patch

def get_patch(idx: int) -> Tuple[Any, Dict[str, Any]]
Reads a single patch from the slide.
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idx
int
required
Index into specs.
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returns
Tuple[Any, Dict[str, Any]]
A tuple (patch, metadata) where patch is an np.ndarray of shape (H, W, C) (or whatever the transform returns), and metadata is a dict with at minimum source, x, y, width, height, slide_name, and tissue_ratio.
Raises:
  • IndexError — If idx is out of range.

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close

def close() -> None
Closes the underlying WSI reader and releases resources. Example: 
>>> wsi.close()