Skip to main content

PyTorch-based framework that handles segmentation/regression/classification using various DL architectures for medical imaging.

Project description

GaNDLF

Codacy
Code style: black

The Generally Nuanced Deep Learning Framework for segmentation, regression and classification.

GaNDLF all options

Why use this?

  • Supports multiple
    • Deep Learning model architectures
    • Data dimensions (2D/3D)
    • Channels/images/sequences
    • Prediction classes
    • Domain modalities (i.e., Radiology Scans and Digitized Histopathology Tissue Sections)
    • Problem types (segmentation, regression, classification)
    • Multi-GPU (on same machine) training
  • Built-in
    • Nested cross-validation (and related combined statistics)
    • Support for parallel HPC-based computing
    • Support for training check-pointing
    • Support for Automatic mixed precision
  • Robust data augmentation, courtesy of TorchIO
  • Handles imbalanced classes (e.g., very small tumor in large organ)
  • Leverages robust open source software
  • No need to write any code to generate robust models

Citation

Please cite the following article for GaNDLF (full PDF):

@misc{pati2021gandlf,
      title={GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging}, 
      author={Sarthak Pati and Siddhesh P. Thakur and Megh Bhalerao and Spyridon Thermos and Ujjwal Baid and Karol Gotkowski and Camila Gonzalez and Orhun Guley and Ibrahim Ethem Hamamci and Sezgin Er and Caleb Grenko and Brandon Edwards and Micah Sheller and Jose Agraz and Bhakti Baheti and Vishnu Bashyam and Parth Sharma and Babak Haghighi and Aimilia Gastounioti and Mark Bergman and Anirban Mukhopadhyay and Sotirios A. Tsaftaris and Bjoern Menze and Despina Kontos and Christos Davatzikos and Spyridon Bakas},
      year={2021},
      eprint={2103.01006},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Documentation

GaNDLF has extensive documentation and it is arranged in the following manner:

Contributing

Please see the contributing guide for more information.

Weekly Meeting

The GaNDLF development team hosts a weekly meeting to discuss feature additions, issues, and general future directions. If you are interested to join, please send us an email!

Disclaimer

  • The software has been designed for research purposes only and has neither been reviewed nor approved for clinical use by the Food and Drug Administration (FDA) or by any other federal/state agency.
  • This code (excluding dependent libraries) is governed by the Apache License, Version 2.0 provided in the LICENSE file unless otherwise specified.

Contact

For more information or any support, please post on the Discussions section.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

GANDLF-0.0.16.dev20230513.tar.gz (157.9 kB view details)

Uploaded Source

Built Distribution

GANDLF-0.0.16.dev20230513-py3-none-any.whl (206.1 kB view details)

Uploaded Python 3

File details

Details for the file GANDLF-0.0.16.dev20230513.tar.gz.

File metadata

  • Download URL: GANDLF-0.0.16.dev20230513.tar.gz
  • Upload date:
  • Size: 157.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for GANDLF-0.0.16.dev20230513.tar.gz
Algorithm Hash digest
SHA256 3ffed742e6b717f4b2a022b1396c1e0e017eeed74183052738c0f3ade42031d5
MD5 1baa816c5cecf55350e831708a2e3caf
BLAKE2b-256 4a7be3b24f649c1589289cf42e7bd25276e8385333096f1489f34e1656fa3f51

See more details on using hashes here.

File details

Details for the file GANDLF-0.0.16.dev20230513-py3-none-any.whl.

File metadata

File hashes

Hashes for GANDLF-0.0.16.dev20230513-py3-none-any.whl
Algorithm Hash digest
SHA256 a37a71940cd7f39025ca8525722302366859122d3b547ed721ba44cc85ecf13f
MD5 c2961d42be4bd17b68c57d7316d29b71
BLAKE2b-256 0a9de78c07c46c2d8fb130b09b5ea4eaffe922f645fcb6b532027ba49d829467

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page