Breast Cancer Mitosis Detector

Overview

This model takes a 64 x 64 PNG image file extracted from the whole slide image as input, and outputs the predicted probability of the image containing mitosis. The model consists of a modified ResNet-50 model trained on the TUPAC16 auxiliary mitosis dataset. For more information and additional features, check out the deep-histopath repository on GitHub.

Model Metadata

Domain Application Industry Framework Training Data Input Data Format
Vision Image Classification Health Care Keras TUPAC16 64×64 PNG Image

References

Licenses

Component License Link
Model Github Repository Apache 2.0 LICENSE
Training Data Custom License TUPAC16

Options available for deploying this model

This model can be deployed using the following mechanisms:

  • Deploy from Dockerhub:

    docker run -it -p 5000:5000 codait/max-breast-cancer-mitosis-detector
    
  • Deploy on Red Hat OpenShift:

    Follow the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial and specify codait/max-breast-cancer-mitosis-detector as the image name.

  • Deploy on Kubernetes:

    kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Breast-Cancer-Mitosis-Detector/master/max-breast-cancer-mitosis-detector.yaml
    

    A more elaborate tutorial on how to deploy this MAX model to production on IBM Cloud can be found here.

  • Locally: follow the instructions in the model README on GitHub

Example Usage

You can test or use this model

Test the model using cURL

Once deployed, you can test the model from the command line. For example if running locally:

curl -F "image=@assets/true.png" -XPOST http://localhost:5000/model/predict
{
  "predictions": [
    {
      "probability": 0.9884441494941711
    }
  ],
  "status": "ok"
}

Test the model in a serverless app

You can utilize this model in a serverless application by following the instructions in the Leverage deep learning in IBM Cloud Functions tutorial.

  • deep-histopath: Predict breast cancer proliferation scores with TensorFlow, Keras, and Apache Spark

Resources and Contributions

If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.