useful functions for evaluating machine learning models in the context of medical imaging

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  • License: MIT
  • Author: James Meakin
  • Requires: Python <4.0, >=3.10
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Project description

Grand Challenge Metrics

Build Status PyPI version PyPI - Python Version Documentation Status Code style

Grand Challenge Metrics contains useful functions for evaluating machine learning models in the context of medical imaging.

Features

  • Bounding box annotations with Jaccard Index calculations
  • Calculations of bootstrapped ROC curves
  • Scoring for detection tasks
  • Efficient calculation of confusion matrices, jaccard scores, dice scores, hausdorff distances, absolute volume differences, and relative volume differences

Getting Started

Grand Challenge Metrics requires Python 3.10 or above, and can be installed from pip. Please see the Getting Started documentation for more details.

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Avatar for jamesmeakin from gravatar.com jamesmeakin

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  • License: MIT
  • Author: James Meakin
  • Requires: Python <4.0, >=3.10
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0.6.0

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