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soma

soma runs computational pathology experiments from images and labels to predictions, metrics, and reports. It combines preprocessing, frozen foundation-model features, and downstream training through a modular Python API or a YAML pipeline.

The soma pipeline — data, a frozen encoder, a trained decoder, and evaluation.

Documentation · PyPI

Install

Requires Python 3.11 or later:

pip install soma-pathology

The distribution is soma-pathology; the Python package and CLI are soma.

Run an experiment

For slide classification, prepare dataset.csv (sample ID, image path, label) and splits.csv (sample ID, split, fold). The getting started guide shows both files and a complete walkthrough.

from soma import (
    AggregatorConfig,
    EncoderConfig,
    EvalConfig,
    Pipeline,
    PipelineConfig,
    TaskConfig,
    TrainingConfig,
)

config = PipelineConfig(
    dataset_csv="dataset.csv",
    splits_csv="splits.csv",
    output_root="output",
    dataset_type="slide",
    encoder=EncoderConfig(name="phikon"),
    aggregator=AggregatorConfig(name="abmil"),
    task=TaskConfig(name="binary_classification"),
    training=TrainingConfig(epochs=5, learning_rate=1e-4, seed=0),
    evaluation=EvalConfig(metrics=["auroc", "balanced_accuracy"]),
)
result = Pipeline(config).run()
print(result.summary)
print(result.run_dir)

phikon weights are public and download on first use. To extract features once and reuse them across experiments, see the API guide.

Use the CLI

Run the same pipeline from a YAML config:

soma config.yaml
# Equivalent:
python -m soma config.yaml

soma merges the file with its bundled defaults. Start with the task examples or the CLI reference. soma list encoders (and aggregators, decoders, tasks, benchmarks) prints the registered components.

Explore

License

Apache-2.0.

Metadata

Release files for soma-pathology 1.19.0

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