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  <section id="saliency-maps">
<span id="saliency"></span><h1>Saliency Maps<a class="headerlink" href="#saliency-maps" title="Permalink to this heading">¶</a></h1>
<p>Slideflow provides an API for calculating gradient-based pixel attribution (saliency maps), as implemented by <a class="reference external" href="https://github.com/PAIR-code/saliency">PAIR</a>. Saliency maps can be calculated manually (as described below), or interactively in <a class="reference internal" href="../studio/#studio"><span class="std std-ref">Slideflow Studio</span></a>.</p>
<p><a class="reference internal" href="../grad/#slideflow.grad.SaliencyMap" title="slideflow.grad.SaliencyMap"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.grad.SaliencyMap</span></code></a> provides an interface for preparing a saliency map generator from a loaded model (Tensorflow or PyTorch) and calculating maps from preprocessed images. Supported methods include:</p>
<ul class="simple">
<li><p>Vanilla gradients</p></li>
<li><p>Integrated gradients</p></li>
<li><p>Guided integrated gradients</p></li>
<li><p>Blur integrated gradients</p></li>
<li><p>XRAI</p></li>
<li><p>Grad-CAM</p></li>
</ul>
<section id="generating-a-saliency-map">
<h2>Generating a Saliency Map<a class="headerlink" href="#generating-a-saliency-map" title="Permalink to this heading">¶</a></h2>
<p>Creating a saliency map with <a class="reference internal" href="../grad/#slideflow.grad.SaliencyMap" title="slideflow.grad.SaliencyMap"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.grad.SaliencyMap</span></code></a> requires two components: a loaded model and a preprocessed image. Trained models can be loaded from disk with <a class="reference internal" href="../model/#slideflow.model.load" title="slideflow.model.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">slideflow.model.load()</span></code></a>, and the model’s preprocessing function can be prepared with <a class="reference internal" href="../util/#slideflow.util.get_preprocess_fn" title="slideflow.util.get_preprocess_fn"><code class="xref py py-func docutils literal notranslate"><span class="pre">slideflow.util.get_preprocess_fn()</span></code></a>.</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"># Load a trained model and preprocessing function.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;../saved_model&#39;</span><span class="p">)</span>
<span class="n">preprocess</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">get_preprocess_fn</span><span class="p">(</span><span class="s1">&#39;../saved_model&#39;</span><span class="p">)</span>

<span class="c1"># Prepare a SaliencyMap</span>
<span class="n">sal_map</span> <span class="o">=</span> <span class="n">SaliencyMap</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">class_idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>There are several ways you might acquire an image to use for a saliency map. To load an image tile from a whole-slide image, you can index a <code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.WSI</span></code> object:</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"># Load a whole-slide image.</span>
<span class="n">wsi</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">WSI</span><span class="p">(</span><span class="s1">&#39;slide.svs&#39;</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="mi">302</span><span class="p">)</span>

<span class="c1"># Extract a tile using grid indexing.</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">wsi</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">25</span><span class="p">]</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="../_images/saliency_source.jpg"><img alt="../_images/saliency_source.jpg" src="../_images/saliency_source.jpg" style="width: 299px;" /></a>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>Alternatively, if you know the coordinates for an image tile and want to extract it from TFRecords, you can use <a class="reference internal" href="../dataset/#slideflow.Dataset.read_tfrecord_by_location" title="slideflow.Dataset.read_tfrecord_by_location"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Dataset.read_tfrecord_by_location()</span></code></a>:</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"># Load a project and dataset.</span>
<span class="n">P</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">Project</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">P</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="n">tile_um</span><span class="o">=</span><span class="mi">302</span><span class="p">)</span>

<span class="c1"># Get the tile from slide &quot;12345&quot; at location (2000, 2000)</span>
<span class="n">slide</span><span class="p">,</span> <span class="n">image</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">read_tfrecord_by_location</span><span class="p">(</span>
    <span class="n">slide</span><span class="o">=</span><span class="s1">&#39;12345&#39;</span><span class="p">,</span>
    <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Once you have an image and a loaded <code class="docutils literal notranslate"><span class="pre">SaliencyMap</span></code> object, you can calculate a saliency map from the preprocessed image:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">mask</span> <span class="o">=</span> <span class="n">sal_map</span><span class="o">.</span><span class="n">integrated_gradients</span><span class="p">(</span><span class="n">preprocess</span><span class="p">(</span><span class="n">image</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="plotting-a-saliency-map">
<h2>Plotting a Saliency Map<a class="headerlink" href="#plotting-a-saliency-map" title="Permalink to this heading">¶</a></h2>
<p>Once a saliency map has been created, you can plot the image as a heatmap or as an overlay. The <code class="docutils literal notranslate"><span class="pre">slideflow.grad</span></code> submodule includes several utility functions to assist with plotting. For example, to plot a basic heatmap using the <code class="docutils literal notranslate"><span class="pre">inferno</span></code> matplotlib colormap, use <code class="xref py py-func docutils literal notranslate"><span class="pre">slideflow.grad.plot_utils.inferno()</span></code>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">slideflow.grad.plot_utils</span> <span class="kn">import</span> <span class="n">inferno</span>

<span class="n">pil_image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">inferno</span><span class="p">(</span><span class="n">mask</span><span class="p">))</span>
<span class="n">pil_image</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="../_images/saliency_heatmap.jpg"><img alt="../_images/saliency_heatmap.jpg" src="../_images/saliency_heatmap.jpg" style="width: 299px;" /></a>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>To plot this saliency map as an overlay, use <code class="xref py py-func docutils literal notranslate"><span class="pre">slideflow.grad.plot_utils.overlay()</span></code>, passing in both the unprocessed image and the saliency map:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">slideflow.grad.plot_utils</span> <span class="kn">import</span> <span class="n">overlay</span>

<span class="n">overlay_img</span> <span class="o">=</span> <span class="n">overlay</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">mask</span><span class="p">)</span>
<span class="n">pil_image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">overlay_img</span><span class="p">)</span>
<span class="n">pil_image</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="../_images/saliency_overlay.jpg"><img alt="../_images/saliency_overlay.jpg" src="../_images/saliency_overlay.jpg" style="width: 299px;" /></a>
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<div class="line"><br /></div>
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</section>
<section id="complete-example">
<h2>Complete Example<a class="headerlink" href="#complete-example" title="Permalink to this heading">¶</a></h2>
<p>The following is a complete example for how to calculate and plot a saliency map for an image tile taken from a whole-slide image.</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="kn">from</span> <span class="nn">slideflow.grad</span> <span class="kn">import</span> <span class="n">SaliencyMap</span>
<span class="kn">from</span> <span class="nn">slideflow.grad.plot_utils</span> <span class="kn">import</span> <span class="n">overlay</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>

<span class="c1"># Load a slide and find the desired image tile.</span>
<span class="n">wsi</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">WSI</span><span class="p">(</span><span class="s1">&#39;slide.svs&#39;</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="mi">302</span><span class="p">)</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">wsi</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">]</span>

<span class="c1"># Load a model and preprocessing function.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="o">../</span><span class="n">saved_model</span><span class="p">)</span>
<span class="n">preprocess</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">get_preprocess_fn</span><span class="p">(</span><span class="s1">&#39;../saved_model&#39;</span><span class="p">)</span>

<span class="c1"># Prepare the saliency map</span>
<span class="n">sal_map</span> <span class="o">=</span> <span class="n">SaliencyMap</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">class_idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="c1"># Calculate saliency map using integrated gradients.</span>
<span class="n">ig_map</span> <span class="o">=</span> <span class="n">sal_map</span><span class="o">.</span><span class="n">integrated_gradients</span><span class="p">(</span><span class="n">preprocess</span><span class="p">(</span><span class="n">image</span><span class="p">))</span>

<span class="c1"># Display the saliency map as an overlay.</span>
<span class="n">overlay_img</span> <span class="o">=</span> <span class="n">overlay</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">ig_map</span><span class="p">)</span>
<span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">overlay_img</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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