Skip to main content

GLAIR - An AI model for Glioblastoma Latent-space Artificial Intelligence Rendering

Project description

#GLAIR: Glioblastoma A.I for developers

GLAIR is a dual-interface deep learning architecture for Glioblastoma rendering. It supports many different models including Generative Variational Autoencoders for time series progression prediction, and convolutional neural networks for tumor classification. As part of our open source mission, this backend serves as a portal for accessing GLAIR's models and architecture through Python.

##Installation

###Install with pip

pip install glair

#and run with

import glair

This project is licensed under the MIT LICENSE

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

glair-1.0-py3-none-any.whl (9.8 kB view details)

Uploaded Python 3

File details

Details for the file glair-1.0-py3-none-any.whl.

File metadata

  • Download URL: glair-1.0-py3-none-any.whl
  • Upload date:
  • Size: 9.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.5

File hashes

Hashes for glair-1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 96d00dfd50f7af9edebf203e96a78ba55490c9567b3c3457cb18ee9f7595545c
MD5 d5215452192a9fab6df761531656797e
BLAKE2b-256 dd098bc566e154ce4aef43e59fa58ba19d569a7f6a363184b9491346acdc92c9

See more details on using hashes here.

Supported by

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