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- app.py +4 -4
.gitignore
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.vscode
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app.py
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@@ -92,13 +92,13 @@ def to_oberlay_image(data):
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# %% ../main.ipynb 6
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title = "Glomerulus Segmentation"
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description = """
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A web app
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The model deployed here is a [
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The provided example images are random subset of kidney slices from the [Human Protein Atlas](https://www.proteinatlas.org/). These have been collected separately from model training and have neither been part of the training nor test set.
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"""
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#article="<p style='text-align: center'><a href='Blog post URL' target='_blank'>Blog post</a></p>"
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examples = [str(p) for p in get_image_files("example_images")]
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# %% ../main.ipynb 6
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title = "Glomerulus Segmentation"
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description = """
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A web app that segments glomeruli in histological kidney slices!
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The model deployed here is a [UNet++](https://arxiv.org/abs/1807.10165) with an [efficientnet-b4](https://arxiv.org/abs/1905.11946) encoder from the [segmentation_models_pytorch](https://github.com/qubvel/segmentation_models.pytorch) library.
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The provided example images are random subset of kidney slices from the [Human Protein Atlas](https://www.proteinatlas.org/). These have been collected separately from model training and have neither been part of the training, validation nor test set.
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See corresponding [blog post](https://fhatje.github.io/posts/glomseg/train_model.html).
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"""
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#article="<p style='text-align: center'><a href='Blog post URL' target='_blank'>Blog post</a></p>"
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examples = [str(p) for p in get_image_files("example_images")]
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