slide2vec 3.2.1

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.

Installation

pip install slide2vec

Install the full model runtime only when you need embedding/model execution:

pip install "slide2vec[models]"

slide2vec now keeps the base install focused on the core package surface and moves the heavier model stack into the optional models extra.

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
coordinates = embedded.coordinates

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(
        target_spacing_um=0.5,
        target_tile_size_px=224,
        tissue_threshold=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.

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
• 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 20 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/.

python -m slide2vec --config-file /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

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