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+# Examples of PathML usage
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+These examples assume you are all set with ``PathML`` installation, let's get started with some analyses you can easily replicate:
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+1. [Load over 160+ different types of pathology images using PathML](https://github.com/Dana-Farber-AIOS/pathml/blob/master/examples/loading_images_vignette.ipynb)
+2. [H&E Stain Deconvolution and Color Normalization](https://github.com/Dana-Farber-AIOS/pathml/blob/master/examples/stain_normalization.ipynb)
+3. [Brightfield imaging pipeline: load an image, preprocess it on a local cluster, and get it read for machine learning analyses in PyTorch](https://github.com/Dana-Farber-AIOS/pathml/blob/master/examples/workflow_HE_vignette.ipynb)
+4. [Multiparametric Imaging: Quickstart & single-cell quantification](https://github.com/Dana-Farber-AIOS/pathml/blob/master/examples/multiplex_if.ipynb)
+5. [Multiparametric Imaging: CODEX & nuclei quantization](https://github.com/Dana-Farber-AIOS/pathml/blob/master/examples/codex.ipynb)
+6. [Train HoVer-Net model to perform nucleus detection and classification, using data from PanNuke dataset](https://github.com/Dana-Farber-AIOS/pathml/blob/master/examples/train_hovernet.ipynb)
+7. [Gallery of PathML preprocessing and transformations](https://github.com/Dana-Farber-AIOS/pathml/blob/master/examples/pathml_gallery.ipynb)
+8. Other examples from the [Rosenthal et al. 2022 paper](https://aacrjournals.org/mcr/article/20/2/202/678062/Building-Tools-for-Machine-Learning-and-Artificial) can be found in the [`vignettes`](https://github.com/Dana-Farber-AIOS/pathml/tree/master/examples/vignettes) folder 
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+<img src="https://github.com/Dana-Farber-AIOS/pathml/assets/25375373/502c9e69-e988-4d61-b50f-0d6bfc8af251" width="1000px" />
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