slide2vec 5.1.1

Embedding of whole slide images with Foundation Models

slide2vec

slide2vec is a Python package for efficient encoding of whole-slide images using publicly available foundation models. It builds on hs2p for fast preprocessing and exposes a focused surface around Model, Pipeline, and ExecutionOptions.

Documentation site: https://clemsgrs.github.io/slide2vec/

Installation

pip install slide2vec
pip install "slide2vec[fm]"

slide2vec keeps the base install focused on the core package surface. Use slide2vec[fm] when you want the PyPI-hosted FM dependencies.

Some model backends still rely on upstream Git repositories that PyPI will not accept as package metadata. Install those separately when needed:

pip install git+https://github.com/lilab-stanford/MUSK.git
pip install git+https://github.com/Mahmoodlab/CONCH.git
pip install git+https://github.com/prov-gigapath/prov-gigapath.git

AtlasPatch-backed tissue segmentation is available through hs2p's sam2 path in the bundled install.

Python API

from slide2vec import Model
from slide2vec.utils.config import hf_login

hf_login()

model = Model.from_preset("virchow2")
embedded = model.embed_slide("/path/to/slide.svs")

tile_embeddings = embedded.tile_embeddings
x = embedded.x
y = embedded.y

Use list_models() when you want to inspect the shipped presets programmatically:

from slide2vec import list_models

all_models = list_models()
tile_models = list_models("tile")
slide_models = list_models("slide")
patient_models = list_models("patient")

Use Pipeline(...) for manifest-driven batch processing when you want artifacts written to disk instead of only in-memory outputs:

from slide2vec import ExecutionOptions, Pipeline, PreprocessingConfig

pipeline = Pipeline(
    model=model,
    preprocessing=PreprocessingConfig(
        requested_spacing_um=0.5,
        requested_tile_size_px=224,
        masks={"min_coverage": {"tissue": 0.1}},
    ),
    execution=ExecutionOptions(output_dir="outputs/demo"),
)
result = pipeline.run(manifest_path="/path/to/slides.csv")

By default, ExecutionOptions() uses all available GPUs. Set ExecutionOptions(num_gpus=4) when you want to cap the sharding explicitly.

Hierarchical Feature Extraction

Tile embeddings can be spatially grouped into regions for downstream models that consume region-level structure. Enable it by setting region_tile_multiple on PreprocessingConfig:

preprocessing = PreprocessingConfig(
    requested_spacing_um=0.5,
    requested_tile_size_px=224,
    region_tile_multiple=6,  # 6x6 tiles per region
)
embedded = model.embed_slide("/path/to/slide.svs", preprocessing=preprocessing)

Hierarchical outputs have shape (num_regions, tiles_per_region, feature_dim) and are written to hierarchical_embeddings/ when persisted.

See docs/python-api.md for details.

Input Manifest

Manifest-driven runs use the schema below. mask_path and spacing_at_level_0 are optional.

sample_id,image_path,mask_path,spacing_at_level_0
slide-1,/path/to/slide-1.svs,/path/to/mask-1.png,0.25
slide-2,/path/to/slide-2.svs,,
...

Use spacing_at_level_0 when the slide file reports a missing or incorrect level-0 spacing and you want to override it.

Outputs

The package writes explicit artifact directories:

• tile_embeddings/<sample_id>.pt or .npz
• tile_embeddings/<sample_id>.meta.json
• hierarchical_embeddings/<sample_id>.pt or .npz (when region_tile_multiple is set)
• hierarchical_embeddings/<sample_id>.meta.json
• slide_embeddings/<sample_id>.pt or .npz
• slide_embeddings/<sample_id>.meta.json
• optional slide_latents/<sample_id>.pt or .npz

.pt remains the default format. .npz is available through ExecutionOptions(output_format="npz").

Supported Models

slide2vec currently ships preset configs for 17 tile-level models and 3 slide-level models.
For the full catalog and preset names, see docs/models.md.

CLI

The CLI is a thin wrapper over the package API.
Bundled configs live under slide2vec/configs/preprocessing/ and slide2vec/configs/models/.

slide2vec /path/to/config.yaml

By default, manifest-driven CLI runs use all available GPUs. Set speed.num_gpus=4 when you want to cap the sharding explicitly.

New to the CLI or doing batch runs to disk? Start with docs/cli.md for the config-driven workflow, overrides, and common run patterns.

Docker

Docker remains available when you prefer a containerized runtime:

docker pull waticlems/slide2vec:latest
docker run --rm -it \
    -v /path/to/your/data:/data \
    -e HF_TOKEN=<your-huggingface-api-token> \
    waticlems/slide2vec:latest

Documentation

• Documentation website for the polished docs site
• docs/python-api.md for the detailed API reference
• docs/cli.md for the config-driven CLI guide
• docs/models.md for the full supported-model catalog
• tutorials/api_walkthrough.ipynb for a notebook walkthrough of the API