Skip to main content

Feature conditioning for IVADO medical imaging project.

Project description

ivadomed Overview

DOI Coverage Status test status publish package Documentation Status License: MIT Twitter Follow

ivadomed is an integrated framework for medical image analysis with deep learning.

The technical documentation is available here. The more detailed installation instruction is available there

Installation

ivadomed requires Python >= 3.7 and < 3.10 as well as PyTorch == 1.8. We recommend working under a virtual environment, which could be set as follows:

python -m venv ivadomed_env
source ivadomed_env/bin/activate

Install from release (recommended)

Install ivadomed and its requirements from Pypi <https://pypi.org/project/ivadomed/>__:

pip install --upgrade pip
pip install ivadomed

Install from source

Bleeding-edge developments builds are available on the project's master branch on Github. Installation procedure is the following:

git clone https://github.com/neuropoly/ivadomed.git
cd ivadomed
pip install -e .

Contributors

This project results from a collaboration between the NeuroPoly Lab and the Mila. The main funding source is IVADO.

List of contributors

Consult our Wiki(https://github.com/ivadomed/ivadomed/wiki) here for more help

Project details


Download files

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

Source Distribution

ivadomed-2.9.10.tar.gz (191.8 kB view details)

Uploaded Source

Built Distribution

ivadomed-2.9.10-py3-none-any.whl (240.4 kB view details)

Uploaded Python 3

File details

Details for the file ivadomed-2.9.10.tar.gz.

File metadata

  • Download URL: ivadomed-2.9.10.tar.gz
  • Upload date:
  • Size: 191.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for ivadomed-2.9.10.tar.gz
Algorithm Hash digest
SHA256 c209ea93229d5a2ffe5688589b3d9a993db2b03be3d965f640ea51f214f5af14
MD5 f0b1d1efd566ba45c0ca512615a36e2e
BLAKE2b-256 dc93aab64c6543dabddc4f50451af84eeaba8fc76e1eae0b4ecce7740fc3b4d7

See more details on using hashes here.

File details

Details for the file ivadomed-2.9.10-py3-none-any.whl.

File metadata

  • Download URL: ivadomed-2.9.10-py3-none-any.whl
  • Upload date:
  • Size: 240.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for ivadomed-2.9.10-py3-none-any.whl
Algorithm Hash digest
SHA256 c19c47f246c6f942f064617fad60f3d2c5d4d3d39ccaebf8f377ca89d3c25286
MD5 14ccb2d9a3d7c3378e775172a20f463a
BLAKE2b-256 a963294ec438db13073fe992324fd247fffbdf2c097961cd28f5b2dac859fca9

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