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.dev20230413.tar.gz (155.9 kB view details)

Uploaded Source

Built Distribution

GANDLF-0.0.16.dev20230413-py3-none-any.whl (203.9 kB view details)

Uploaded Python 3

File details

Details for the file GANDLF-0.0.16.dev20230413.tar.gz.

File metadata

  • Download URL: GANDLF-0.0.16.dev20230413.tar.gz
  • Upload date:
  • Size: 155.9 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.dev20230413.tar.gz
Algorithm Hash digest
SHA256 0e0d8070f4d2d88233b19b58ac0e8e629597307cc2e56d1d782ee5d966a73c88
MD5 caad0529e4902db3533382f59dd2a576
BLAKE2b-256 0201d35041a492546689e737bd232b9616e1d863e10244a5188450d4354ebd31

See more details on using hashes here.

File details

Details for the file GANDLF-0.0.16.dev20230413-py3-none-any.whl.

File metadata

File hashes

Hashes for GANDLF-0.0.16.dev20230413-py3-none-any.whl
Algorithm Hash digest
SHA256 f6bd604e06b914135cb61c41557127d2f62852583a6bafb15c3bb3144869a3fc
MD5 0295847aa2f3e9300dcc3bc5a92a8e18
BLAKE2b-256 5935a302a0251875c0168337eb43314e8c5d9edb6bcaeb2b75443f19921b1f9f

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