Metadata-Version: 2.1
Name: dlup
Version: 0.4.0
Home-page: https://github.com/NKI-AI/dlup
Author: Jonas Teuwen
Author-email: j.teuwen@nki.nl
License: Apache Software License 2.0
Keywords: dlup
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
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License-File: LICENSE
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# Deep Learning Utilities for Pathology
[![pypi](https://img.shields.io/pypi/v/dlup.svg)](https://pypi.python.org/pypi/dlup)
[![Tox](https://github.com/NKI-AI/dlup/actions/workflows/tox.yml/badge.svg)](https://github.com/NKI-AI/dlup/actions/workflows/tox.yml)
[![mypy](https://github.com/NKI-AI/dlup/actions/workflows/mypy.yml/badge.svg)](https://github.com/NKI-AI/dlup/actions/workflows/mypy.yml)
[![Pylint](https://github.com/NKI-AI/dlup/actions/workflows/pylint.yml/badge.svg)](https://github.com/NKI-AI/dlup/actions/workflows/pylint.yml)
[![Black](https://github.com/NKI-AI/dlup/actions/workflows/black.yml/badge.svg)](https://github.com/NKI-AI/dlup/actions/workflows/black.yml)
[![codecov](https://codecov.io/gh/NKI-AI/dlup/branch/main/graph/badge.svg?token=OIJ7F9G7OO)](https://codecov.io/gh/NKI-AI/dlup)

Dlup offers a set of utilities to ease the process of running Deep Learning algorithms on
Whole Slide Images.


## Features
- Read whole-slide images at any arbitrary resolution by seamlessly interpolating between the pyramidal levels
- Supports multiple backends, including [OpenSlide](https://openslide.org/) and [VIPS](https://libvips.github.io/libvips/), with the possibility to add custom backends
- Dataset classes to handle whole-slide images in a tile-by-tile manner compatible with pytorch
- Annotation classes which can load GeoJSON, [V7 Darwin](https://www.v7labs.com/), [HALO](https://indicalab.com/halo/) and [ASAP](https://computationalpathologygroup.github.io/ASAP/) formats and read parts of it (e.g. a tile)
- Transforms to handle annotations per tile, resulting, together with the dataset classes a dataset consisting of tiles of whole-slide images with corresponding masks as targets, readily useable with a pytorch dataloader
- Command-line utilities to report on the metadata of WSIs, and convert masks to polygons

Check the [full documentation](https://docs.aiforoncology.nl/dlup) for more details on how to use dlup.

## Quickstart
The package can be installed using `python -m pip install dlup`.

## Used by
- [ahcore](https://github.com/NKI-AI/ahcore.git): a pytorch lightning based-library for computational pathology

## Citing DLUP
If you use DLUP in your research, please use the following BiBTeX entry:

```
@software{dlup,
  author = {Teuwen, J., Romor, L., Pai, A., Schirris, Y., Marcus, E.},
  month = {6},
  title = {{DLUP: Deep Learning Utilities for Pathology}},
  url = {https://github.com/NKI-AI/dlup},
  version = {0.4.0},
  year = {2024}
}
```

or the following plain bibliography:

```
Teuwen, J., Romor, L., Pai, A., Schirris, Y., Marcus E. (2024). DLUP: Deep Learning Utilities for Pathology (Version 0.3.38) [Computer software]. https://github.com/NKI-AI/dlup
```
