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Slideflow Studio: Live Visualization

Slideflow Studio provides powerful tools for interactive visualization of whole-slide images, model predictions, and GAN-generated images. It’s also fast - with an OpenGL renderer and highly optimized whole-slide image viewer, you’ll get a smooth experience that can even run on a Raspberry Pi.

If you have installed slideflow via PIP, you can run Studio from a terminal with:


If you are running from source, you can start Studio using the following script in the GitHub repository:

python slideflow-studio.py

If you encounter any issues with the initialization scripts, you can also start Studio by executing the submodule:

python -m slideflow.studio

If you are using a Docker image, additional arguments are required to launch Studio. Start your docker container using the arguments -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix. For example:

docker run -it --rm \
    -v /tmp/.X11-unix:/tmp/.X11-unix \

A path to a whole-slide image can optionally be provided as the first argument. Use the --help flag to see a list of available arguments.

You can also launch Studio by using the .view() function of slideflow.WSI, slideflow.Heatmap, and slideflow.Mosaic functions.

import slideflow

wsi = sf.WSI('/path/to/slide.svs', tile_px=299, tile_um=302)

Layout & design


The Slideflow Studio window has three primary areas: the main view, a tile preview, and the control panel. Fullscreen mode can be toggled with View -> Fullscreen or by pressing Ctrl+F.

Main view

The main view is an interactive display for whole-slide images. Zoom in on a slide using the mouse wheel, and navigate around the slide by clicking and dragging. When a model is loaded, right clicking on the main view sets the prediction location, drawing a red box outlining the location that a tile was extracted and displaying the prediction underneath.

Tile preview

When a model is loaded, right clicking on the main view will establish the location for a focal tile prediction. A tile will be extracted from this location of the whole-slide image at the pixel & micron size appropriate for the loaded model. The tile preview window shows the extracted image tile taken from this location. If the loaded model uses stain normalization, a post-normalization image is also shown on the right. The tile preview window can be hidden by clicking the X in the top right corner, or toggled via the menu item View -> Show -> Tile Preview.

Control panel

The control panel shows relevant active widgets which contain information and controls for whole-slide images, loaded models, heatmaps, and loaded GANs. Enabling an extension will add an additional icon and associated functionality.


A Slideflow Project can be loaded to make it easier to find and load both slides and models. Load a project with either File -> Open Project, or click and drag a project folder onto the main view. Click the Project icon to view project information and browse both slides and models.


All slides associated with the project will be listed under the “Slides” subheader. Clicking a slide name will open the slide. Similarly, all trained models associated with the project are listed under the “Models” subheader and can be loaded by clicking a model name. Both Tensorflow and PyTorch models can be loaded, regardless of the active backend.

Whole-slide images


Whole-slide images can be loaded directly with File -> Open Slide. You can also load a slide by dragging and dropping a file onto the main view or by using the Project interface. Use the mouse wheel to zoom, and click-and-drag to move. Slides can be closed with File -> Close Slide.

The Slide section of the control panel shows slide properties, including dimensions, highest scanned magnification, slide scanner vendor, and how many annotated regions-of-interest (ROIs) are loaded for the slide. ROIs are loaded automatically if a Project is loaded and ROIs are available for the slide.

A thumbnail of the loaded slide is shown in the upper right corner of the main view, and can be hidden with View -> Show -> Thumbnail. A magnification scale is shown in the bottom-left corner of the main view, and can be hidden with View -> Show -> Scale.

ROI Annotations


Regions-of-Interest (ROIs) can be used to guide tile extraction. If a Slideflow project has been loaded (File -> Open Project), ROIs will be automatically loaded. You can use Studio to add, label, or remove ROIs with the annotation tool, under the subheader “ROIs”.

Click the plus (Add) icon to draw new ROIs with a lasso tool; right click and drag to create a new ROI. The pencil (Edit) icon allows you to edit any existing ROIs; right click an ROI while editing to delete the ROI or change its label. Once finished, ROIs can be exported in CSV format by clicking the floppy disk icon (Save). You can manually load an existing ROI file by clicking the folder icon (Load).

Labels can be optionally supplied for each ROI. Labels can be set after creating an ROI and changed by right clicking an ROI while editing. Hover over an existing ROI to see its name and label. Labels are exported when saving ROIs.

Slideflow can import ROIs generated from external applications such as QuPath and ImageScope; see Regions of Interest for more information.

Tile filtering


A tile filtering strategy can be applied by checking “Tile filter” in the “Slide Processing” subsection. Click the ellipsis button to change grayspace fraction/threshold and whitespace fraction/threshold, to see how tuning these parameters alters tile-level filtering. If enabled, tile filtering will be performed when generating predictions from the slide. Once enabled, the tile filter can be previewed by checking the box “Show tile-level filter” in the “Display” subsection.

Slide filtering


Similarly, slide filtering can be enabled by checking “Slide filter”. Available slide filtering / QC options include blur filtering, Otsu’s thresholding, or both. If “Tile filter” and “Slide filter” are both selected, tiles will be filtered with both. The QC mask can be previewed by checking the box “QC Mask” in the “Display” subsection.

Tissue segmentation

New in version 2.3, trained segmentation models can be deployed as a slide filter. Start by typing or pasting the path to a trained segmentation model in the text box next to “Segment”. Then, check “Segment” to generate and apply a QC mask. The QC mask can be previewed by checking the box “QC Mask” in the “Display” subsection. The QC mask is constructed from all pixels predicted to be background.

In addition to using a segmentation model for QC, a trained model can be used to generate labeled ROIs. Click “Generate ROIs” to generate ROIs from the segmentation model. The ROIs will be shown in the main view, and can be saved with the annotation tool. The ROIs will be saved in CSV format, with the ROI label corresponding to the predicted class.

Preview slide normalization

Stain normalization strategies can be quickly previewed by checking “Normalize”, which will apply the associated normalization strategy to the main view. If a model is loaded, the model’s normalizer will be used by default. The normalizer can be changed with the corresponding dropdown menu, allowing you to preview any normalization method. All normalizer methods shown except for the model normalizer will use the “v3” fit (see slideflow.norm for more information). Regardless of what is being previewed, the appropriate model normalizer will be used when generating predictions from the slide.

Preview tile extraction


The “Display” subsection of the slide widget allows users to preview tile extraction, displaying outlines around tiles. Model predictions generated from the slide will only utilize the shown tiles.

Models & predictions

Slideflow models can be loaded with File -> Open Model, by clicking and dragging a model onto the main view, or by clicking “Load a Model” button of the model widget. Both Tensorflow and PyTorch models are supported. Multiple-instance learning (MIL) models require the MIL extension, as discussed below. Models can be closed with File -> Close Model.

A summary of the loaded model is shown on the left side of the model widget, containing information about the model outcomes, tile size, image format (PNG/JPG), backend (Tensorflow/PyTorch), and the version of Slideflow used to train the model. Click the “HP” button to show a list of all hyperparameters used during model training.

A model will be enabled by default once loaded, but can be disabled by clicking the gear icon in the Model section of the control panel, and then clicking “Close model”. Similarly, to disable uncertainty quantification (UQ) for models trained with UQ, open the same gear menu and deselect “Enable UQ”.

Tile predictions


Once a model is loaded, right-click anywhere on the main view to set the tile extraction location for the tile preview. A tile will be extracted at this location matching the pixel and micron size of the loaded model. The extracted tile will be shown before and after stain normalization (if applicable) in the tile preview window. Right click and drag to slide the preview window. The model prediction at this location will be shown underneath the red box in the main view, and in histogram format in the control panel, along with the class label for categorical models.



Saliency maps for the given model and image tile can be previewed in real-time by selecting the checkbox under the “Saliency” subheader. The saliency map will replace the extracted image tile in the tile preview window. Alternatively, saliency can be viewed as an overlay on top of the extracted image tile by checking the box “Overlay”. The dropdown menu below in this section can be used to change the saliency method.

Slide predictions


Click the “Predict Slide” button to generate a prediction for the whole-slide image. By default, this will show predictions across the slide as a heatmap in the main display, and the final prediction for the slide will be shown under the “Slide Prediction” subheader of the control panel. Histograms of predictions for each model outcome, as well as uncertainty (if applicable), will be shown in this same section of the control panel. Click the + and - buttons in this section to cycle through histograms for each outcome category.



The heatmap section of the control panel can be used to generate and customize whole-slide heatmaps. Heatmaps are generated using the settings configured in the Slide section of the control panel (including stride, tile filter, and slide filter). Click “Generate” in the heatmap widget to create the heatmap. The color scheme can be changed with the dropdown menu of the “Display” subheader, as can the alpha and gain. You can switch which outcome is being displayed as a heatmap by cycling through the available predictions. If the model was trained with uncertainty quantification (UQ), click the radio button next to UQ to show uncertainty as a heatmap. Press the left ALT key while hovering over the heatmap to show the raw heatmap values.

By default, heatmaps are calculated with multiprocessing pools, which may increase memory utilization. To decrease memory utilization at the cost of slower heatmap calculation, switch to low memory mode in the Settings section (described below), or by using the launch flag --low_memory.

Heatmaps can be saved in PNG format with File -> Export -> Heatmap (PNG). Heatmaps can also be exported in numpy format (NPZ) with File -> Export -> Heatmap (NPZ). The heatmap of predictions will be saved in the exported NPZ file under the key 'logit', with the shape (y_dim, x_dim, num_classes). If the model was trained with uncertainty, the uncertainty heatmap will be saved under the key 'uncertainty'.

Performance & Capture


Performance can be monitored in the Performance section of the control panel (lightning icon). This section shows frametimes for GUI display, image rendering, normalization, and model prediction.

Export contents of the main view to a PNG file with File -> Export -> Main view. Similarly, the extracted image tile shown in the tile preview window can be exported with File -> Export -> Tile view. A screenshot of the entire window interface can be saved with File -> Export -> GUI view.


Studio can be customized in the Settings section, which provides the ability to set a FPS limit (defaults to 60), enable vertical sync (enabled by default), and customize the theme. This section also includes an option to enter “Low lemory mode”. In low memory mode, heatmaps are calculated with threadpools rather than multiprocessing pools, decreasing memory utilization at the cost of slower heatmap generation.



Slideflow Studio includes an Extensions section for expanding functionality and adding additional features. Extensions may require additional software dependencies or have different licenses. The Extensions section can be accessed by clicking the puzzle icon in bottom-left section of the control panel.

Four official extensions are included and described below, adding support for cell segmentation with Cellpose, generative adversarial networks (StyleGAN), mosaic maps, and multiple-instance learning. Development is underway to add support for community extensions that can be shared and downloaded. Please reach out to us on GitHub if you are interested in building and deploying an extension based on your research.

Cell segmentation

The Cell Segmentation extension adds support for interactive cell segmentation with Cellpose. Please see Cell Segmentation for more information.


The StyleGAN extension adds support for visualizing trained StyleGAN2 or StyleGAN3 networks. Once enabled, GAN *.pkl files can be loaded with File -> Load GAN, or with drag-and-drop. Generated images are shown in the tile preview window. Model predictions on GAN images operate similarly to predictions on whole-slide images. Predictions on GAN images are generated in real-time, and you can watch the predictions change in the control panel.

By default, Studio will generate predictions on the full GAN image (after resizing to match the model’s tile_px value). If a training_options.json file is found in the same directory as the GAN .pkl, the tile size used to train the GAN will be read from this file (slideflow_kwargs/tile_px and ../tile_um). If the GAN was trained on images with a different tile_um value, the GAN image will be cropped to match the model’s tile_um before resizing. The cropped/resized (and stain normalized) image will be shown to the right of the raw GAN image in the tile preview window.

The StyleGAN widget can be used to travel the GAN latent space, similar to the implementation in the official NVIDIA StyleGAN3 repository. Set a specific seed in the input field next to “Seed”, or click and drag the “Drag” button. If the model was trained with class conditioning, manually set the class with the “Class” field (the default value of -1 selects a random class). Press left or right on your keyboard to quickly move through seeds.

The style mixing section can be used to mix styles between seeds, styles between classes, or both. You can control the degree of mixing with the mixing slider. You can finetune which GAN layers are used during the mixing by clicking the ellipsis button and selection which layers should be traversed during style mixing.

Save the current seed by clicking the “Save” button; all saved seeds will be listed in the “Saved Seeds” subsection. Click any seed to load it. Once any seed has been saved, options will appear to export a list of saved seeds in CSV format. Previously exported seeds can be loaded by clicking “Load Seeds”.

Multiple-Instance Learning

Slideflow Studio includes support for multiple-instance learning (MIL) models with the MIL extension. In addition to generating predictions from MIL models, Studio can also be used to visualize associated attention heatmaps. Please see Multiple-Instance Learning (MIL) for more information.

Start by navigating to the Extensions tab in the bottom-left corner, and enable the “Multiple-instance Learning” extension. A new icon will appear in the left-hand toolbar, which can be used to open the MIL widget. Models are loaded by either clicking the “Load MIL model” button, selecting “File -> Load MIL Model…”, or by dragging-and-dropping an MIL model folder onto the window.

Information about the feature extractor and MIL model will be shown in the left-hand toolbar. MIL model architecture and hyperparameters can be viewed by clicking the “HP” button. Click “Predict Slide” to generate a whole-slide prediction. If applicable, attention will be displayed as a heatmap. The heatmap color and display can be customized in the Heatmap widget.

Right-clicking for a focal prediction when an MIL model is loaded will display the tile-level attention along with the tile prediction. Tile-level attention can be displayed as a scaled colorbar, as shown in the video above, by specifying an attention range and thresholds in the MIL mil_params.json file.

    "thresholds": {
        "attention": {
            "low": 0.3,
            "high": 0.5,
            "range": [0, 1]

Mosaic maps

The Mosaic Maps extension, which is enabled by default, adds support for interactively viewing mosaic maps. You can use the slideflow.Mosaic.view() function to launch Studio and load the mosaic.

import slideflow as sf

mosaic = sf.Mosaic(...)

Alternatively, a mosaic map can be saved to disk with slideflow.Mosaic.export(), and then loaded into Studio with File -> Load Mosaic.


Once loaded,the mosaic map can be navigated using the same controls as WSI navigation - click and drag to pan, and use the mouse wheel to zoom. The UMAP used to generate the mosaic map will be shown in a window in the bottom-right corner, with a red box indicating the section of the UMAP currently in view. If a Project is loaded, hovering over an image tile will reveal a popup containing a larger corresponding section from the associated whole-slide image. This popup also contains the name of the slide and tile location coordinates.

Use the control panel to increase or decrease the mosaic grid size, or to change the background color.