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.dev20230326.tar.gz (134.9 kB view details)

Uploaded Source

Built Distribution

GANDLF-0.0.16.dev20230326-py3-none-any.whl (178.9 kB view details)

Uploaded Python 3

File details

Details for the file GANDLF-0.0.16.dev20230326.tar.gz.

File metadata

  • Download URL: GANDLF-0.0.16.dev20230326.tar.gz
  • Upload date:
  • Size: 134.9 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.dev20230326.tar.gz
Algorithm Hash digest
SHA256 e12595aa8d9828818627728d812655e778b288d7f8ac6b3afd6a51d761e2640b
MD5 8323fde81102afabbd6331c7f53429a4
BLAKE2b-256 8fbc9f5eb566cc14fcab417d2bf5fa6812fc7e04fe78631c082a71fd159c1cdb

See more details on using hashes here.

File details

Details for the file GANDLF-0.0.16.dev20230326-py3-none-any.whl.

File metadata

File hashes

Hashes for GANDLF-0.0.16.dev20230326-py3-none-any.whl
Algorithm Hash digest
SHA256 39c6b4d679ff2c630d5543cc4784752c1468163a273c4beec13e585c036953b9
MD5 4d0d588fd5557070e08f2a2752e71852
BLAKE2b-256 ec81e895c33ee883976795e15cd3a37c2e04135b7c2261fce524e50e2273234e

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