capricorn-ai 0.1.0

Deep learning histopathology classification with Keras

README.md

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Capricorn AI

Deep-learning based histopathology image classifier built with TensorFlow Keras.

Overview

Capricorn AI provides pre-trained Keras models for classifying H&E–stained tissue patches into diagnostic categories. It offers a simple Python API to load different model versions, run inference on images, and integrate into larger pipelines.

Installation

# Clone the repo
git clone https://github.com/SigMoses/Capricorn.git
cd capricorn

# Install dependencies
pip install -r requirements.txt

# Install as a package
pip install .

Alternatively, install directly from GitHub:

pip install git+https://github.com/SigMoses/Capricorn.git

Quickstart

from capricorn import list_models, load_model, load_image, predict

# List available models
print(list_models())  # ['capricorn0.1']

# Load an image
img = load_image('path/to/slide_patch.png')

# Run prediction
detections = predict('capricorn0.1', img)
print(detections)

Example Usage

from capricorn import load_image, predict, ALL_LABELS, label_confidences

# Load and preprocess an image patch
img = load_image("path/to/patch.png")

# Run inference (returns shape (1, NUM_CLASSES))
probs = predict("capricorn0.1", img)[0]

# Print all label confidences in descending order
for label, confidence in label_confidences(probs):
    print(f"{label:30s} {confidence*100:5.1f}%")

Available Models

• capricorn0.1: Initial version trained on PathMNIST subsets.

Add your own retrained models by placing the .keras file in capricorn/ai_models/ and updating capricorn/ai_models.py.

Development & Testing

• Dependencies are in requirements.txt.

• Run tests with:

  pytest
  

Contributing

1. Fork the repository.
2. Create a new branch (git checkout -b feature/my-model).
3. Add your .keras model in capricorn/ai_models/ and register it in capricorn/models.py.
4. Update docs and add tests.
5. Submit a pull request.

Credits

• Original Progenitor
  Capricorn AI was conceived and developed by Moshe Newman.

• Dataset
  This project relies on the MedMNIST dataset. Please credit the MedMNIST team as follows:

  Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni.
  “MedMNIST v2—A large-scale lightweight benchmark for 2D and 3D biomedical image classification.” Scientific Data, 2023.

  Jiancheng Yang, Rui Shi, Bingbing Ni.
  “MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis.” IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.

• Usage & Modification
  You are free to modify or use this code as you please, provided that you retain proper credit to Moshe Newman as the original progenitor of Capricorn AI, and credit the MedMNIST team for the dataset as cited above.

License

MIT License