.. slideflow documentation master file

.. figure:: https://i.imgur.com/YrsKN4I.jpeg

Slideflow Documentation

=======================

Slideflow is a Python package that provides a unified API for building and testing deep learning models for histopathology, supporting both Tensorflow/Keras and PyTorch.

Slideflow includes tools for efficient whole-slide image processing, easy and highly customizable model training with uncertainty quantification (UQ), and a number of functional tools to assist with analysis and interpretability, including predictive heatmaps, mosaic maps, GANs, saliency maps, and more. It is built with both `Tensorflow/Keras <https://www.tensorflow.org/>`_ and `PyTorch <https://pytorch.org>`_ backends, with fully cross-compatible TFRecord data storage.

This documentation starts with a high-level overview of the pipeline and includes examples of how to perform common tasks using the ``Project`` helper class. We also provide several tutorials with examples of how Slideflow can be used and extended for additional functionality.

.. toctree::

   :maxdepth: 1

   :caption: Introduction

   installation

   overview

   quickstart

   project_setup

   datasets_and_val

   slide_processing

   training

   evaluation

   posthoc

   uq

   features

   mil

   ssl

   stylegan

   saliency

   segmentation

   cellseg

   custom_loops

   studio

   troubleshooting

.. toctree::

   :maxdepth: 1

   :caption: Developer Notes

   tfrecords

   dataloaders

   custom_extractors

   tile_labels

   plugins

.. toctree::

   :maxdepth: 1

   :caption: API

   slideflow

   project

   dataset

   dataset_features

   heatmap

   model_params

   mosaic

   slidemap

   biscuit

   slideflow_cellseg

   io

   io_tensorflow

   io_torch

   gan

   grad

   mil_module

   model

   model_tensorflow

   model_torch

   norm

   simclr

   slide

   slide_qc

   stats

   util

   studio_module

.. toctree::

   :maxdepth: 1

   :caption: Tutorials

   tutorial1

   tutorial2

   tutorial3

   tutorial4

   tutorial5

   tutorial6

   tutorial7

   tutorial8