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Medicai

Medic-AI is a Keras based library designed for medical image analysis using machine learning techniques. It provides seamless compatibility with multiple backends, allowing models to run on tensorflow, torch, and jax.

Note: It is currently in its early stages and will undergo multiple iterations before reaching a stable release.

Installation

git clone https://github.com/innat/medic-ai
cd medic-ai
pip install . -q

Available Features

The medicai library provides a range of features for medical image processing, model training, and inference. Below is an overview of its key functionalities.

Image Transformations

medicai includes various transformation utilities for preprocessing medical images:

  • Basic Transformations:
    • Resize – Adjusts the image dimensions.
    • ScaleIntensityRange – Normalizes intensity values within a specified range.
    • CropForeground – Crops the image to focus on the region of interest.
    • Spacing – Resamples the image to a target voxel spacing.
    • Orientation – Standardizes image orientation.
  • Augmentations for Robustness:
    • RandRotate90 – Randomly rotates images by 90 degrees.
    • RandShiftIntensity – Randomly shifts intensity values.
    • RandFlip – Randomly flips images along specified axes.
  • Pipeline Composition:
    • Compose – Chains multiple transformations into a single pipeline.

Models

Currently, medicai focuses on 3D models for classification and segmentation:

  • SwinTransformer – 3D classification task.
  • SwinUNETR – 3D segmentation task.

Inference

  • SlidingWindowInference – Processes large 3D images in smaller overlapping windows, improving performance and memory efficiency.

Guides (WIP)

  • 3D transformation
  • 3D classification
  • 3D Segmentation

Acknowledgements

This project is greatly inspired by MONAI.

Citation

If you use medicai in your research, please cite it using the metadata from our CITATION.cff file.