.. Active Segmentation documentation master file, created by
   sphinx-quickstart on Sat Nov  6 14:42:24 2021.
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Active Segmentation
=======================================

*An active learning benchmarking framework for scientists seeking comparability and reproducibility.*

``active segmentation`` provides a flexible and expressive API for evaluating active learning strategies for
medical image segmentation.

With ``active segmentation``, you can:

#. Define own segmentation models by implementing :py:meth:`models.pytorch_model.PytorchModel`, or use pre-configured :ref:`models<models>` like the U-Net.
#. Add own datasets by implementing :py:meth:`datasets.data_module.ActiveLearningDataModule`, or use already added :ref:`datasets<datasets>` like the
   `medical segmentation decathlon <http://medicaldecathlon.com>`_.
#. Evaluate active learning :ref:`query strategies<query_strategies`, or define and test new strategies by implementing :py:meth:`query_strategies.query_strategy.QueryStrategy`.
#. Train two-dimensional or three-dimensional segmentation models.
#. Run fully reproducible experiments, with seeded random processes and only deterministic operations.
#. Track various :ref:`metrics<metric_tracking>` with `Weights and Biases <https://wandb.ai/>`_.

Issues
------

Submit issues, feature requests or bugfixes on
`github <https://github.com/HealthML/active-segmentation/issues>`__.

How to Cite
-----------

If you use ``active segmentation`` in the context of academic or industry research, please
consider citing the paper.

`Paper <https://arxiv.org/abs/2207.00845>`_
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: bash

    @misc{https://doi.org/10.48550/arxiv.2207.00845,
          doi = {10.48550/ARXIV.2207.00845},
          url = {https://arxiv.org/abs/2207.00845},
          author = {Burmeister, Josafat-Mattias and Fernandez Rosas, Marcel and Hagemann, Johannes and Kordt, Jonas and Blum, Jasper and Shabo, Simon and Bergner, Benjamin and Lippert, Christoph},
          keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.4.6; I.2.10; J.3},
          title = {Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation},
          publisher = {arXiv},
          year = {2022},
          copyright = {arXiv.org perpetual, non-exclusive license}
         }

License
-------------------

``active segmentation`` is licensed under the `AGPL-3.0 license <https://github.com/HealthML/active-segmentation/blob/main/LICENSE>`_.


.. toctree::
   :maxdepth: 2
   :caption: Introduction
   :hidden:

   self

.. toctree::
   :maxdepth: 2
   :caption: Contents
   :hidden:

   configuration
   active_segmentation
   extensions

Indices and tables
-------------------

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`