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.dev20230501.tar.gz (158.0 kB view details)

Uploaded Source

Built Distribution

GANDLF-0.0.16.dev20230501-py3-none-any.whl (206.1 kB view details)

Uploaded Python 3

File details

Details for the file GANDLF-0.0.16.dev20230501.tar.gz.

File metadata

  • Download URL: GANDLF-0.0.16.dev20230501.tar.gz
  • Upload date:
  • Size: 158.0 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.dev20230501.tar.gz
Algorithm Hash digest
SHA256 e5eab7065b1d39c664dce5d5dba5707a56aaa6010a2a9da072df08c6989d1199
MD5 fa0eae4863920a5f1c50f0d71fd43416
BLAKE2b-256 98e8c819d63415f5090447480d618f0b63dbd6150010892f7d96819095ad5183

See more details on using hashes here.

File details

Details for the file GANDLF-0.0.16.dev20230501-py3-none-any.whl.

File metadata

File hashes

Hashes for GANDLF-0.0.16.dev20230501-py3-none-any.whl
Algorithm Hash digest
SHA256 e649292738f9e2b004c9bed17a68d53a60f600ad28625ed9eec001d4eaf323a4
MD5 244623cacf25e59f04db5441000473a0
BLAKE2b-256 65d7ae0f2648408b44c0d4068f3537bcfdbb2d1da8bbfa55e78369e5ea5407d5

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