PathML

Scope and safety boundary

Use PathML for local computational pathology research. It is beta research software, not a validated medical device, diagnostic system, clinical decision support tool, or substitute for a pathologist. Do not use outputs to diagnose, grade, stage, or treat a patient.

Pathology files may contain faces, labels, accession numbers, patient identifiers, DICOM tags, filenames, or linked clinical data. Before processing:

1. Confirm authorization, consent/waiver, data-use terms, and institutional policy.
2. De-identify pixels and metadata; keep the re-identification key outside the analysis workspace.
3. Use pseudonymous patient_id, slide_id, and specimen_id values. Do not put direct identifiers in filenames, logs, .h5path labels, model cards, or reports.
4. Keep inputs, intermediates, and outputs on approved local encrypted storage.
5. Split by patient (then slide) before tiling or fitting any preprocessing step.

Version baseline, reviewed 2026-10-01

• Published stable release: PyPI pathml==3.0.8, published 2026-08-14, source tag v3.0.8 (fa49ffb66757ed8a8265756c92b6290834eddecb).
• The v3.0.5 release notes state Python 3.10-3.12 and sunset 3.9. PyPI does not declare Requires-Python and still has a stale 3.8 classifier, so use the release statement and test the exact environment.
• The current wheel incorporates the Torch/PyG dependency updates and explicit legacy ONNX export (dynamo=False). ReadTheDocs pages/search caches can show different versions; the 3.0.8 wheel and tagged source determine these APIs.
• Bundled CLIs are tested with synthetic data. PathML examples and installation commands are illustrative, source-checked, not a full native-stack or pathology-model execution claim. No slides, model weights, or datasets were downloaded for this review.
• This skill is MIT-licensed. PathML itself is GPL-2.0 with upstream commercial licensing options; review upstream terms before redistribution.

Reproducible installation

Use Python 3.11 unless the project has tested another supported interpreter:

uv venv --python 3.11
source .venv/bin/activate
uv pip install "pathml==3.0.8"
python -c "import importlib.metadata as m; print(m.version('pathml'))"

PathML 3.0.8 declares no package extras: do not use pathml[all]. Its base distribution pins a large scientific/ML stack, including Torch 2.12.0, ONNX 1.22.0, ONNX Runtime 1.17.x, OpenSlide Python 1.3.1, python-bioformats 4.1.0, and python-javabridge 4.0.4, torch-geometric 2.8.0, and onnxscript 0.7.1. It also pins NumPy below 2 and several older binary packages; do not upgrade them independently.

Install native prerequisites before the uv command:

# Debian/Ubuntu
sudo apt-get install openslide-tools gcc g++ libblas-dev liblapack-dev openjdk-17-jdk

# macOS
brew install openslide openjdk@17

# Windows OpenSlide option documented upstream
vcpkg install openslide

python-javabridge==4.0.4 is source-only on PyPI and needs a JDK (including javac/JNI headers), not just a JRE. Set JAVA_HOME to that JDK before building. PathML imports the Java bridge/Bio-Formats in its shared backend module even when the requested reader is OpenSlide; selecting OpenSlide does not avoid those installation dependencies. Old import-error advice mentioning Java 8 and javabridge==1.0.19 does not match the released wheel requirements.

Java/Bio-Formats provides the broad multidimensional format backend. OpenSlide handles common brightfield WSI formats more efficiently. CUDA is optional and must match the pinned PyTorch build; follow PyTorch's platform selector rather than guessing a CUDA wheel. See references/image_loading.md.

Stable minimal workflow

PathML 3.0.8 uses slide convenience classes and SlideData.run(). It does not provide SlideData.from_slide(), and Pipeline does not have run():

from pathml.core import HESlide
from pathml.preprocessing import BoxBlur, Pipeline, TissueDetectionHE

slide = HESlide("data/pseudonymous_slide.svs", backend="openslide")
pipeline = Pipeline(
    [
        BoxBlur(kernel_size=5),
        TissueDetectionHE(mask_name="tissue", min_region_size=5000),
    ]
)
slide.run(
    pipeline,
    distributed=False,
    tile_size=512,
    tile_stride=512,
    level=0,
    tile_pad=False,
)
slide.write("derived/pseudonymous_slide.h5path")

Start with a bounded manual sample before a full run:

…