Quickstart¶

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.

Preparing a project¶

Slideflow experiments are organized using slideflow.Project, which supervises storage of data, saved models, and results. The slideflow.project module has three preconfigured projects with associated slides and clinical annotations: LungAdenoSquam, ThyroidBRS, and BreastER.

For this example, we will the LungAdenoSquam project to train a classifier to predict lung adenocarcinoma (Adeno) vs. squamous cell carcinoma (Squam).

import slideflow as sf

# Download preconfigured project, with slides and annotations.
project = sf.create_project(
    root='data',
    cfg=sf.project.LungAdenoSquam(),
    download=True
)

Read more about setting up a project on your own data.

Data preparation¶

The core imaging data used in Slideflow are image tiles extracted from slides at a specific magnification and pixel resolution. Tile extraction and downstream image processing is handled through the primitive slideflow.Dataset. We can request a Dataset at a given tile size from our project using slideflow.Project.dataset(). Tile magnification can be specified in microns (as an int) or as optical magnification (e.g. '40x').

# Prepare a dataset of image tiles.
dataset = project.dataset(
    tile_px=299,   # Tile size, in pixels.
    tile_um='10x'  # Tile size, in microns or magnification.
)
dataset.summary()

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(['patient', 'subtype', 'site', 'slide'],
    dtype='object')

Tile extraction¶

We prepare imaging data for training by extracting tiles from slides. Background areas of slides will be filtered out with Otsu’s thresholding.

# Extract tiles from all slides in the dataset.
dataset.extract_tiles(qc='otsu')

Read more about tile extraction and slide processing in Slideflow.

Held-out test sets¶

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 slideflow.Dataset.split(). We’ll split while balancing the outcome 'subtype' equally in the training and test dataset, with 30% of the data retained in the held-out set.

# Split our dataset into a training and held-out test set.
train_dataset, test_dataset = dataset.split(
    model_type='classification',
    labels='subtype',
    val_fraction=0.3
)

Configuring models¶

Neural network models are prepared for training with slideflow.ModelParams, 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 architectures and loss functions can be customized. In this example, we will use the included Xception network.

# Prepare a model and hyperparameters.
params = sf.ModelParams(
    tile_px=299,
    tile_um='10x',
    model='xception',
    batch_size=64,
    learning_rate=0.0001
)

Training a model¶

Models can be trained from these hyperparameter configurations using Project.train(). Models can be trained to categorical, multi-categorical, continuous, or time-series outcomes, and the training process is highly configurable. In this case, we are training a binary categorization model to predict the outcome 'subtype', and we will distribute training across multiple GPUs.

By default, Slideflow will train/validate on the full dataset using k-fold cross-validation, but validation settings can be customized. 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 train_dataset, and allow Slideflow to further split this into training and validation using three-fold cross-validation.

# Train a model from a set of hyperparameters.
results = P.train(
    'subtype',
    dataset=train_dataset,
    params=params,
    val_strategy='k-fold',
    val_k_fold=3,
    multi_gpu=True,
)

Models and training results will be saved in the project models/ folder.

Evaluating a trained model¶

After training, you can test model performance on a held-out test dataset with Project.evaluate(), or generate predictions without evaluation (when ground-truth labels are not available) with Project.predict(). As with Project.train(), we can specify a slideflow.Dataset to evaluate.

# Train a model from a set of hyperparameters.
test_results = P.evaluate(
    model='/path/to/trained_model_epoch1'
    outcomes='subtype',
    dataset=test_dataset
)

Post-hoc analysis¶

Slideflow includes a number of analytical tools for working with trained models. Read more about heatmaps, model explainability, analysis of layer activations, and real-time inference in an interactive whole-slide image reader.