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# QuPath extension WSInfer
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[![Preprint](https://zenodo.org/badge/DOI/10.48550/arXiv.2309.04631.svg)](https://doi.org/10.48550/arXiv.2309.04631)
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[![Extension docs](https://img.shields.io/badge/docs-qupath_wsinfer-red)](https://qupath.readthedocs.io/en/stable/docs/deep/wsinfer.html)
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[![Forum](https://img.shields.io/badge/forum-image.sc-green)](https://forum.image.sc/tag/wsinfer)
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[![Downloads (latest release)](https://img.shields.io/github/downloads-pre/qupath/qupath-extension-wsinfer/latest/total)](https://github.com/qupath/qupath-extension-wsinfer/releases/latest)
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[![Downloads (all releases)](https://img.shields.io/github/downloads/qupath/qupath-extension-wsinfer/total)](https://github.com/qupath/qupath-extension-wsinfer/releases)
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This repo contains the extension to work with WSInfer models in QuPath.
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This helps make deep learning-based patch classification in pathology images easy and interactive.
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# QuPath WSInfer extension
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See https://wsinfer.readthedocs.io for details.
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This repo contains the QuPath extension that adds support for the [WSInfer Model Zoo](https://wsinfer.readthedocs.io).
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> **If you use this extension, please cite both the [WSInfer preprint](https://arxiv.org/abs/2309.04631) and the [QuPath paper](https://qupath.readthedocs.io/en/0.4/docs/intro/citing.html)!**
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Together, QuPath & WSInfer aim to make deep learning-based patch classification easy and interactive for pathology images.
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To find out more, check out our preprint on arXiv:
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> [Jakub R. Kaczmarzyk, Alan O'Callaghan, Fiona Inglis, Tahsin Kurc, Rajarsi Gupta, Erich Bremer, Peter Bankhead, Joel H. Saltz <br/>
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> *Open and reusable deep learning for pathology with WSInfer and QuPath*. <br/>
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> arXiv, 2023](https://arxiv.org/abs/2309.04631)
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**If you use this extension, please cite both the [WSInfer preprint](https://arxiv.org/abs/2309.04631) and the [QuPath paper](https://qupath.readthedocs.io/en/0.4/docs/intro/citing.html)!**
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## Installation
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The WSInfer extension adds a new menu item to QuPath's **Extensions** menu, which can be used to open a WSInfer dialog.
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The dialog tries to guide you through the main steps, from top to bottom.
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For a step-by-step guide,see [ReadTheDocs](https://qupath.readthedocs.io/en/stable/docs/deep/wsinfer.html).
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Briefly: after selecting a WSInfer model, you'll need to select one or more tiles to use for inference.
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The easiest way to do this is generally to draw an annotation, and leave it up to QuPath to create the tiles.
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Pressing run will download the model and PyTorch (if necessary), then run the model across the tiles.
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You can see the results in the form of measurement maps, as a results table, or as colored tiles in the QuPath viewer.
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> Tip: To see the tiles properly, you'll need to ensure that they are both displayed and filled in the viewer (i.e. ensure the two buttons showing three green objects are selected).
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## Additional options
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It's worth checking out the *Additional options* to see where models will be stored.
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You can also use this to select whether inference should use the CPU or GPU - if a GPU is available and compatible.
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> GPU acceleration is selected by choosing *MPS* on an Apple Silicon Mac, for *Metal Performance Shaders*.
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And if things don't go to plan or you want to discuss more, please post your questions on the [Scientific Community Image Forum (image.sc)](https://forum.image.sc/tag/wsinfer) - and tag both `wsinfer` and `qupath` so we notice.

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