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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"><a class="reference internal" href="../overview/">Overview</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">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>
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<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>
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<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>
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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>
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<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="../slideflow/">slideflow</a></li>
<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>
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<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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<li class="toctree-l1"><a class="reference internal" href="../biscuit/">slideflow.biscuit</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../io/">slideflow.io</a></li>
<li class="toctree-l1"><a class="reference internal" href="../io_tensorflow/">slideflow.io.tensorflow</a></li>
<li class="toctree-l1"><a class="reference internal" href="../io_torch/">slideflow.io.torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../gan/">slideflow.gan</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../mil_module/">slideflow.mil</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="quickstart">
<h1>Quickstart<a class="headerlink" href="#quickstart" title="Permalink to this heading">¶</a></h1>
<p>This section provides an example of using Slideflow to build a deep learning classifier from digital pathology slides. Follow the links in each section for more information.</p>
<section id="preparing-a-project">
<h2>Preparing a project<a class="headerlink" href="#preparing-a-project" title="Permalink to this heading">¶</a></h2>
<p>Slideflow experiments are organized using <a class="reference internal" href="../project/#slideflow.Project" title="slideflow.Project"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.Project</span></code></a>, which supervises storage of data, saved models, and results. The <code class="docutils literal notranslate"><span class="pre">slideflow.project</span></code> module has three preconfigured projects with associated slides and clinical annotations: <code class="docutils literal notranslate"><span class="pre">LungAdenoSquam</span></code>, <code class="docutils literal notranslate"><span class="pre">ThyroidBRS</span></code>, and <code class="docutils literal notranslate"><span class="pre">BreastER</span></code>.</p>
<p>For this example, we will the <code class="docutils literal notranslate"><span class="pre">LungAdenoSquam</span></code> project to train a classifier to predict lung adenocarcinoma (Adeno) vs. squamous cell carcinoma (Squam).</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">slideflow</span> <span class="k">as</span> <span class="nn">sf</span>

<span class="c1"># Download preconfigured project, with slides and annotations.</span>
<span class="n">project</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">create_project</span><span class="p">(</span>
    <span class="n">root</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">,</span>
    <span class="n">cfg</span><span class="o">=</span><span class="n">sf</span><span class="o">.</span><span class="n">project</span><span class="o">.</span><span class="n">LungAdenoSquam</span><span class="p">(),</span>
    <span class="n">download</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Read more about <a class="reference internal" href="../project_setup/#project-setup"><span class="std std-ref">setting up a project on your own data</span></a>.</p>
</section>
<section id="data-preparation">
<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading">¶</a></h2>
<p>The core imaging data used in Slideflow are image tiles <a class="reference internal" href="../slide_processing/#filtering"><span class="std std-ref">extracted from slides</span></a> at a specific magnification and pixel resolution. Tile extraction and downstream image processing is handled through the primitive <a class="reference internal" href="../datasets_and_val/#datasets-and-validation"><span class="std std-ref">slideflow.Dataset</span></a>. We can request a <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> at a given tile size from our project using <a class="reference internal" href="../project/#slideflow.Project.dataset" title="slideflow.Project.dataset"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.dataset()</span></code></a>. Tile magnification can be specified in microns (as an <code class="docutils literal notranslate"><span class="pre">int</span></code>) or as optical magnification (e.g. <code class="docutils literal notranslate"><span class="pre">'40x'</span></code>).</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Prepare a dataset of image tiles.</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">project</span><span class="o">.</span><span class="n">dataset</span><span class="p">(</span>
    <span class="n">tile_px</span><span class="o">=</span><span class="mi">299</span><span class="p">,</span>   <span class="c1"># Tile size, in pixels.</span>
    <span class="n">tile_um</span><span class="o">=</span><span class="s1">&#39;10x&#39;</span>  <span class="c1"># Tile size, in microns or magnification.</span>
<span class="p">)</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Overview:
╒===============================================╕
│ Configuration file: │ /mnt/data/datasets.json │
│ Tile size (px):     │ 299                     │
│ Tile size (um):     │ 10x                     │
│ Slides:             │ 941                     │
│ Patients:           │ 941                     │
│ Slides with ROIs:   │ 941                     │
│ Patients with ROIs: │ 941                     │
╘===============================================╛

Filters:
╒====================╕
│ Filters:      │ {} │
├--------------------┤
│ Filter Blank: │ [] │
├--------------------┤
│ Min Tiles:    │ 0  │
╘====================╛

Sources:

TCGA_LUNG
╒==============================================╕
│ slides    │ /mnt/raid/SLIDES/TCGA_LUNG       │
│ roi       │ /mnt/raid/SLIDES/TCGA_LUNG       │
│ tiles     │ /mnt/rocket/tiles/TCGA_LUNG      │
│ tfrecords │ /mnt/rocket/tfrecords/TCGA_LUNG/ │
│ label     │ 299px_10x                        │
╘==============================================╛

Number of tiles in TFRecords: 0
Annotation columns:
Index([&#39;patient&#39;, &#39;subtype&#39;, &#39;site&#39;, &#39;slide&#39;],
    dtype=&#39;object&#39;)
</pre></div>
</div>
<section id="tile-extraction">
<h3>Tile extraction<a class="headerlink" href="#tile-extraction" title="Permalink to this heading">¶</a></h3>
<p>We prepare imaging data for training by extracting tiles from slides. Background areas of slides will be filtered out with Otsu’s thresholding.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extract tiles from all slides in the dataset.</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">extract_tiles</span><span class="p">(</span><span class="n">qc</span><span class="o">=</span><span class="s1">&#39;otsu&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Read more about tile extraction and <a class="reference internal" href="../slide_processing/#filtering"><span class="std std-ref">slide processing in Slideflow</span></a>.</p>
</section>
<section id="held-out-test-sets">
<h3>Held-out test sets<a class="headerlink" href="#held-out-test-sets" title="Permalink to this heading">¶</a></h3>
<p>Now that we have our dataset and we’ve completed the initial tile image processing, we’ll split the dataset into a training cohort and a held-out test cohort with <a class="reference internal" href="../dataset/#slideflow.Dataset.split" title="slideflow.Dataset.split"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Dataset.split()</span></code></a>. We’ll split while balancing the outcome <code class="docutils literal notranslate"><span class="pre">'subtype'</span></code> equally in the training and test dataset, with 30% of the data retained in the held-out set.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Split our dataset into a training and held-out test set.</span>
<span class="n">train_dataset</span><span class="p">,</span> <span class="n">test_dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">split</span><span class="p">(</span>
    <span class="n">model_type</span><span class="o">=</span><span class="s1">&#39;classification&#39;</span><span class="p">,</span>
    <span class="n">labels</span><span class="o">=</span><span class="s1">&#39;subtype&#39;</span><span class="p">,</span>
    <span class="n">val_fraction</span><span class="o">=</span><span class="mf">0.3</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Read more about <a class="reference internal" href="../datasets_and_val/#datasets-and-validation"><span class="std std-ref">Dataset management</span></a>.</p>
</section>
</section>
<section id="configuring-models">
<h2>Configuring models<a class="headerlink" href="#configuring-models" title="Permalink to this heading">¶</a></h2>
<p>Neural network models are prepared for training with <a class="reference internal" href="../model_params/#slideflow.ModelParams" title="slideflow.ModelParams"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.ModelParams</span></code></a>, through which we define the model architecture, loss, and hyperparameters. Dozens of architectures are available in both the Tensorflow and PyTorch backends, and both neural network <a class="reference internal" href="../tutorial3/#tutorial3"><span class="std std-ref">architectures</span></a> and <a class="reference internal" href="../training/#custom-loss"><span class="std std-ref">loss</span></a> functions can be customized. In this example, we will use the included Xception network.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Prepare a model and hyperparameters.</span>
<span class="n">params</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">ModelParams</span><span class="p">(</span>
    <span class="n">tile_px</span><span class="o">=</span><span class="mi">299</span><span class="p">,</span>
    <span class="n">tile_um</span><span class="o">=</span><span class="s1">&#39;10x&#39;</span><span class="p">,</span>
    <span class="n">model</span><span class="o">=</span><span class="s1">&#39;xception&#39;</span><span class="p">,</span>
    <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
    <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0001</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Read more about <a class="reference internal" href="../training/#training"><span class="std std-ref">hyperparameter optimization in Slideflow</span></a>.</p>
</section>
<section id="training-a-model">
<h2>Training a model<a class="headerlink" href="#training-a-model" title="Permalink to this heading">¶</a></h2>
<p>Models can be trained from these hyperparameter configurations using <code class="xref py py-meth docutils literal notranslate"><span class="pre">Project.train()</span></code>. Models can be trained to categorical, multi-categorical, continuous, or time-series outcomes, and the training process is <a class="reference internal" href="../training/#training"><span class="std std-ref">highly configurable</span></a>. In this case, we are training a binary categorization model to predict the outcome <code class="docutils literal notranslate"><span class="pre">'subtype'</span></code>, and we will distribute training across multiple GPUs.</p>
<p>By default, Slideflow will train/validate on the full dataset using k-fold cross-validation, but validation settings <a class="reference internal" href="../datasets_and_val/#validation-planning"><span class="std std-ref">can be customized</span></a>. If you would like to restrict training to only a subset of your data - for example, to leave a held-out test set untouched - you can manually specify a dataset for training. In this case, we will train on <code class="docutils literal notranslate"><span class="pre">train_dataset</span></code>, and allow Slideflow to further split this into training and validation using three-fold cross-validation.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Train a model from a set of hyperparameters.</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
    <span class="s1">&#39;subtype&#39;</span><span class="p">,</span>
    <span class="n">dataset</span><span class="o">=</span><span class="n">train_dataset</span><span class="p">,</span>
    <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">,</span>
    <span class="n">val_strategy</span><span class="o">=</span><span class="s1">&#39;k-fold&#39;</span><span class="p">,</span>
    <span class="n">val_k_fold</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
    <span class="n">multi_gpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Models and training results will be saved in the project <code class="docutils literal notranslate"><span class="pre">models/</span></code> folder.</p>
<p>Read more about <a class="reference internal" href="../training/#training"><span class="std std-ref">training a model</span></a>.</p>
</section>
<section id="evaluating-a-trained-model">
<h2>Evaluating a trained model<a class="headerlink" href="#evaluating-a-trained-model" title="Permalink to this heading">¶</a></h2>
<p>After training, you can test model performance on a held-out test dataset with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Project.evaluate()</span></code>, or generate predictions without evaluation (when ground-truth labels are not available) with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Project.predict()</span></code>. As with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Project.train()</span></code>, we can specify a <a class="reference internal" href="../dataset/#slideflow.Dataset" title="slideflow.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.Dataset</span></code></a> to evaluate.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Train a model from a set of hyperparameters.</span>
<span class="n">test_results</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
    <span class="n">model</span><span class="o">=</span><span class="s1">&#39;/path/to/trained_model_epoch1&#39;</span>
    <span class="n">outcomes</span><span class="o">=</span><span class="s1">&#39;subtype&#39;</span><span class="p">,</span>
    <span class="n">dataset</span><span class="o">=</span><span class="n">test_dataset</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Read more about <a class="reference internal" href="../evaluation/#evaluation"><span class="std std-ref">model evaluation</span></a>.</p>
</section>
<section id="post-hoc-analysis">
<h2>Post-hoc analysis<a class="headerlink" href="#post-hoc-analysis" title="Permalink to this heading">¶</a></h2>
<p>Slideflow includes a number of analytical tools for working with trained models. Read more about <a class="reference internal" href="../evaluation/#evaluation"><span class="std std-ref">heatmaps</span></a>, <a class="reference internal" href="../stylegan/#stylegan"><span class="std std-ref">model explainability</span></a>, <a class="reference internal" href="../posthoc/#activations"><span class="std std-ref">analysis of layer activations</span></a>, and real-time inference in an interactive <a class="reference internal" href="../studio/#studio"><span class="std std-ref">whole-slide image reader</span></a>.</p>
</section>
</section>


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