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              <p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../installation/">Installation</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../quickstart/">Quickstart</a></li>
<li class="toctree-l1"><a class="reference internal" href="../project_setup/">Setting up a Project</a></li>
<li class="toctree-l1"><a class="reference internal" href="../datasets_and_val/">Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../slide_processing/">Slide Processing</a></li>
<li class="toctree-l1"><a class="reference internal" href="../training/">Training</a></li>
<li class="toctree-l1"><a class="reference internal" href="../evaluation/">Evaluation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../posthoc/">Layer Activations</a></li>
<li class="toctree-l1"><a class="reference internal" href="../uq/">Uncertainty Quantification</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/">Generating Features</a></li>
<li class="toctree-l1"><a class="reference internal" href="../mil/">Multiple-Instance Learning (MIL)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ssl/">Self-Supervised Learning (SSL)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../stylegan/">Generative Networks (GANs)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../saliency/">Saliency Maps</a></li>
<li class="toctree-l1"><a class="reference internal" href="../segmentation/">Tissue Segmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../cellseg/">Cell Segmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../custom_loops/">Custom Training Loops</a></li>
<li class="toctree-l1"><a class="reference internal" href="../studio/">Slideflow Studio: Live Visualization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../troubleshooting/">Troubleshooting</a></li>
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<p class="caption" role="heading"><span class="caption-text">Developer Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../tfrecords/">TFRecords: Reading and Writing</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dataloaders/">Dataloaders: Sampling and Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../custom_extractors/">Custom Feature Extractors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tile_labels/">Strong Supervision with Tile Labels</a></li>
<li class="toctree-l1"><a class="reference internal" href="../plugins/">Creating a Slideflow Plugin</a></li>
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<p class="caption" role="heading"><span class="caption-text">API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../project/">slideflow.Project</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dataset/">slideflow.Dataset</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dataset_features/">slideflow.DatasetFeatures</a></li>
<li class="toctree-l1"><a class="reference internal" href="../heatmap/">slideflow.Heatmap</a></li>
<li class="toctree-l1"><a class="reference internal" href="../model_params/">slideflow.ModelParams</a></li>
<li class="toctree-l1"><a class="reference internal" href="../mosaic/">slideflow.Mosaic</a></li>
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<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../tutorial1/">Tutorial 1: Model training (simple)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorial2/">Tutorial 2: Model training (advanced)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorial3/">Tutorial 3: Using a custom architecture</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorial4/">Tutorial 4: Model evaluation &amp; heatmaps</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorial5/">Tutorial 5: Creating a mosaic map</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorial6/">Tutorial 6: Custom slide filtering</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorial7/">Tutorial 7: Training with custom augmentations</a></li>
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  <section id="overview">
<h1>Overview<a class="headerlink" href="#overview" title="Permalink to this heading">¶</a></h1>
<p>Slideflow provides tools for easily building and testing a variety of deep learning models for digital pathology.</p>
<p>This section provides a high-level overview of the most common application: building and testing a weakly supervised predictive model. Slideflow supports many other tasks, including <a class="reference internal" href="../mil/#mil"><span class="std std-ref">multiple-instance learning (MIL)</span></a>, <a class="reference internal" href="../ssl/#simclr-ssl"><span class="std std-ref">self-supervised learning (SSL)</span></a>, <a class="reference internal" href="../stylegan/#stylegan"><span class="std std-ref">generative adversarial networks (GANs)</span></a>, <a class="reference internal" href="../segmentation/#segmentation"><span class="std std-ref">tissue</span></a> and <a class="reference internal" href="../cellseg/#cellseg"><span class="std std-ref">cell</span></a> segmentation, and <a class="reference internal" href="../studio/#studio"><span class="std std-ref">deployment &amp; visualization</span></a>, which are discussed in subsequent sections.</p>
<figure class="align-default" id="id1">
<img alt="../_images/overview.png" src="../_images/overview.png" />
<figcaption>
<p><span class="caption-text"><em>High-level overview of model building.</em></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
<p>The pipeline for a deep learning classification experiment is separated into three phases.</p>
<ol class="arabic simple">
<li><p><strong>Tile extraction</strong> - annotate slides with regions of interest (ROIs) [<em>optional</em>] and extract image tiles from whole-slide images.</p></li>
<li><p><strong>Model training</strong> - determine model parameters, train a model, and evaluate the model on a held-out test set.</p></li>
<li><p><strong>Explainability</strong> - generate predictive heatmaps and analyze learned image features.</p></li>
</ol>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>A brief introduction to the steps needed to execute a basic experiment is provided below. Each process will be described in more detail in the following sections.</p>
<section id="step-1-prepare-a-dataset">
<h2>Step 1: Prepare a dataset<a class="headerlink" href="#step-1-prepare-a-dataset" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><strong>Extract tiles</strong>. <a class="reference internal" href="../slide_processing/#filtering"><span class="std std-ref">Tiles are extracted</span></a> from slides at a given magnification size in microns (or a magnification layer, such as “10x”), and saved at a given resolution in pixels. The optimal extraction size in both microns and pixels will depend on your dataset and model architecture. Poor quality tiles - including background tiles or tiles with high whitespace content - can be discarded with quality control methods. Tiles will be stored as TFRecords, a binary file format used to improve dataset reading performance during training. Each slide will have its own TFRecord file containing its extracted tiles.</p></li>
<li><p><strong>Set aside final evaluation set</strong>. <a class="reference internal" href="../datasets_and_val/#datasets-and-validation"><span class="std std-ref">Split the dataset</span></a> into a training/validation set and held-out test set.</p></li>
<li><p><strong>Determing validation plan</strong>. By default, three-fold cross-validation will be performed during training. Many other validation strategies are also supported (<a class="reference internal" href="../datasets_and_val/#validation-planning"><span class="std std-ref">Training/Validation Splitting</span></a>).</p></li>
</ul>
</section>
<section id="step-2-train-a-model">
<h2>Step 2: Train a model<a class="headerlink" href="#step-2-train-a-model" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><strong>Choose model type</strong>. Choose the endpoint (e.g. classification, regression, time-to-event) and type of model (tile-based or multiple-instance learning).</p></li>
<li><p><strong>Set hyperparameters</strong>. Choose a model architecture (e.g. InceptionV3, VGG16, ResNet, etc.) and a set of hyperparameters (e.g. batch size, learning rate, etc.). This can be done manually, or <a class="reference internal" href="../training/#hyperparameter-optimization"><span class="std std-ref">hyperparameters can be optimized</span></a> via grid search or Bayesian optimization.</p></li>
<li><p><strong>Initiate training</strong>. <a class="reference internal" href="../training/#training"><span class="std std-ref">Train your model</span></a>, taking note of training and validation performance (e.g. accuracy, AUROC, AP, R-squared, C-index).</p></li>
</ul>
</section>
<section id="step-3-evaluate-the-model">
<h2>Step 3: Evaluate the model<a class="headerlink" href="#step-3-evaluate-the-model" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><strong>Evaluate on held-out set</strong>: <a class="reference internal" href="../evaluation/#evaluation"><span class="std std-ref">Evaluate your final model</span></a> model on the held-out dataset.</p></li>
</ul>
</section>
<section id="step-4-generate-heatmaps">
<h2>Step 4: Generate heatmaps<a class="headerlink" href="#step-4-generate-heatmaps" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><strong>Generate heatmaps</strong>: <a class="reference internal" href="../evaluation/#generate-heatmaps"><span class="std std-ref">Generate heatmaps</span></a> of predictions across slides in the held-out dataset to assist with interpretability. For MIL models, heatmaps of both predictions and attention can be generated.</p></li>
</ul>
<img alt="../_images/heatmap_example.png" src="../_images/heatmap_example.png" />
</section>
<section id="step-5-make-a-mosaic-map">
<h2>Step 5: Make a Mosaic map<a class="headerlink" href="#step-5-make-a-mosaic-map" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><strong>Generate a mosaic map</strong>: <a class="reference internal" href="../posthoc/#mosaic-map"><span class="std std-ref">Create a mosaic map</span></a>, which visually illustrates the latent space of your trained model and held-out dataset, to assist with interpretability.</p></li>
</ul>
<img alt="../_images/mosaic_example.png" src="../_images/mosaic_example.png" />
</section>
<section id="step-6-live-visualization">
<h2>Step 6: Live visualization<a class="headerlink" href="#step-6-live-visualization" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><strong>Deploy the model</strong>: Finally, use a trained model to visualize predictions for whole-slide images with the interactive tool <a class="reference internal" href="../studio/#studio"><span class="std std-ref">Slideflow Studio</span></a>. This whole-slide image viewer includes deep learning tools enabling you to visualize model predictions on whole-slide images, standard JPG/PNG files, real-time camera feeds, and even Generative Adversarial Network (GAN)-generated images.</p></li>
</ul>
<img alt="../_images/workbench_preview.png" src="../_images/workbench_preview.png" />
</section>
</section>


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<li><a class="reference internal" href="#step-1-prepare-a-dataset">Step 1: Prepare a dataset</a></li>
<li><a class="reference internal" href="#step-2-train-a-model">Step 2: Train a model</a></li>
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