medicai 0.0.1

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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
%cd ..

Guide

• 3D transformation
• 3D classification
• 3D segmentation

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.

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.