Setting up a Project

Slideflow Projects organize datasets, annotations, and results into a unified directory and provide a high-level API for common tasks.

Use slideflow.create_project() to create a new project, supplying an annotations file (with patient labels) and path to slides. A new dataset source (collection of slides and tfrecords) will be configured. Additional keyword arguments can be used to specify the location of trecords and saved models.

import slideflow as sf

P = sf.create_project(
  root='project_path',
  annotations="./annotations.csv"
  slides='/path/to/slides/'
)

Project settings are saved in a settings.json file in the root project directory. Each project will have the following settings:

name	Project name. Defaults to “MyProject”.
annotations	Path to CSV containing annotations. Each line is a unique slide. Defaults to “./annotations.csv”
dataset_config	Path to JSON file containing dataset configuration. Defaults to “./datasets.json”
sources	Names of dataset source(s) to include in the project. Defaults to an empty list.
models_dir	Path, where model files and results are saved. Defaults to “./models”
eval_dir	Path, where model evaluation results are saved. Defaults to “./eval”

Once a project has been initialized at a directory, you may then load the project with the following syntax:

import slideflow as sf
P = sf.load_project('/path/to/project/directory')

Dataset Sources

A dataset source is a collection of slides, Regions of Interest (ROI) annotations (if available), and extracted tiles. Sources are defined in the project dataset configuration file, which can be shared and used across multiple projects or saved locally within a project directory. These configuration files have the following format:

{
  "SOURCE":
  {
    "slides": "/directory",
    "roi": "/directory",
    "tiles": "/directory",
    "tfrecords": "/directory",
  }
}

When a project is created with slideflow.create_project(), a dataset source is automatically created. You can change where slides and extracted tiles are stored by editing the project’s dataset configuration file.

It is possible for a project to have multiple dataset sources - for example, you may choose to organize data from multiple institutions into separate sources. You can add a new dataset source to a project with Project.add_source(), which will update the project dataset configuration file accordingly.

P.add_source(
  name="SOURCE_NAME",
  slides="/slides/directory",
  roi="/roi/directory",
  tiles="/tiles/directory",
  tfrecords="/tfrecords/directory"
)

Read more about working with datasets.

Annotations

Your annotations file is used to label patients and slides with clinical data and/or other outcome variables that will be used for training. Each line in the annotations file should correspond to a unique slide. Patients may have more than one slide.

The annotations file may contain any number of columns, but it must contain the following headers at minimum:

• patient: patient identifier

• slide: slide name / identifier (without the file extension)

An example annotations file is given below:

patient	category	dataset	slide
TCGA-EL-A23A	EGFR-mutant	train	TCGA-EL-A3CO-01Z-00-DX1-7BF5F
TCGA-EL-A35B	EGFR-mutant	eval	TCGA-EL-A35B-01Z-00-DX1-89FCD
TCGA-EL-A26X	non-mutant	train	TCGA-EL-A26X-01Z-00-DX1-4HA2C
TCGA-EL-B83L	non-mutant	eval	TCGA-EL-B83L-01Z-00-DX1-6BC5L

An example annotations file is generated each time a new project is initialized. To manually generate an empty annotations file that contains all detected slides, use the bundled Project function:

P.create_blank_annotations()

The slide column may not need to be explicitly set in the annotations file by the user. Rather, once a dataset has been set up, slideflow will search through the linked slide directories and attempt to match slides to entries in the annotations file using patient. Entries that are blank in the slide column will be auto-populated with any detected and matching slides, if available.