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.
Waiv encoders use a separately tested Transformers 5 runtime. Install them in
their own environment with pip install "slide2vec[waiv]"; the waiv extra is
incompatible with the existing fm, prism, and titan dependency pins.
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 the hierarchical features guide 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>.ptor.npztile_embeddings/<sample_id>.meta.jsonhierarchical_embeddings/<sample_id>.ptor.npz(whenregion_tile_multipleis set)hierarchical_embeddings/<sample_id>.meta.jsonslide_embeddings/<sample_id>.ptor.npzslide_embeddings/<sample_id>.meta.json- optional
slide_latents/<sample_id>.ptor.npz
.pt remains the default format. .npz is available through ExecutionOptions(output_format="npz").
Supported Models
slide2vec currently ships presets for 28 tile-level models, 4 slide-level models,
and 1 patient-level model.
For the full catalog and preset names, see the model zoo.
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 the CLI guide for the config-driven workflow 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
- API guide
- CLI guide
- Model zoo
- Performance benchmarks and QA
tutorials/api_walkthrough.ipynbfor a notebook walkthrough of the API
1 maintainer
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