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[DOC] add disclaimer that this project is for academic research purposes only #224

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merged 4 commits into from
Jul 9, 2024
Merged

[DOC] add disclaimer that this project is for academic research purposes only #224

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6302c59
add academic research use warning to readme
kaczmarj Jul 9, 2024
7a867ce
add academic research disclaimer
kaczmarj Jul 9, 2024
385ce1f
update float and int types for slide coords and probs
kaczmarj Jul 9, 2024
c8354f5
do not ignore types when making binary im arr
kaczmarj Jul 9, 2024
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3 changes: 3 additions & 0 deletions README.md
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Expand Up @@ -12,6 +12,9 @@ Original H&E | Heatmap of Tumor Probability
[![Supported Python versions](https://img.shields.io/pypi/pyversions/wsinfer)](https://pypi.org/project/wsinfer/)
[![Published in npj Precision Oncology](https://img.shields.io/badge/Published-npj_Precision_Oncology-blue)](https://doi.org/10.1038/s41698-024-00499-9)

> [!CAUTION]
> WSInfer is an academic project intended for research use only.

See https://wsinfer.readthedocs.io for documentation.

The main feature of WSInfer is a minimal command-line interface for running deep learning inference
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4 changes: 4 additions & 0 deletions docs/index.rst
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Expand Up @@ -23,6 +23,10 @@ WSInfer: blazingly fast inference on whole slide images
on whole slide images. It includes several built-in models, and it can be used with any
PyTorch model as well. The built-in models :ref:`are listed below <available-models>`.

.. caution::

WSInfer is an academic project intended for research use only.

Running inference on whole slide images is done with a single command line:

::
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4 changes: 2 additions & 2 deletions wsinfer/modellib/run_inference.py
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Expand Up @@ -171,8 +171,8 @@ def run_inference(

# Store the coordinates and model probabiltiies of each patch in this slide.
# This lets us know where the probabiltiies map to in the slide.
slide_coords: list[npt.NDArray[np.int_]] = []
slide_probs: list[npt.NDArray[np.float_]] = []
slide_coords: list[npt.NDArray[np.integer]] = []
slide_probs: list[npt.NDArray[np.floating]] = []
for batch_imgs, batch_coords in tqdm.tqdm(loader):
assert batch_imgs.shape[0] == batch_coords.shape[0], "length mismatch"
with torch.no_grad():
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5 changes: 1 addition & 4 deletions wsinfer/patchlib/segment.py
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Expand Up @@ -66,10 +66,7 @@ def segment_tissue(
_, im_arr = cv.threshold(
im_arr, thresh=binary_threshold, maxval=255, type=cv.THRESH_BINARY
)

# Convert to boolean dtype. This helps with static type analysis because at this
# point, im_arr is a uint8 array.
im_arr_binary: npt.NDArray[np.bool_] = im_arr > 0 # type: ignore
im_arr_binary: npt.NDArray[np.bool_] = im_arr > 0

# Closing. This removes small holes. It might not be entirely necessary because
# we have hole removal below.
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