Detect whether a mitosis exists in an image of breast cancer tumor cells
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README.md

IBM Code Model Asset Exchange: Breast Cancer Mitosis Detector

The Tumor Proliferation Assessment Challenge 2016 (TUPAC16) was created to develop state-of-the-art algorithms for automatic prediction of tumor proliferation scores from whole-slide histopathology images of breast tumors. The IBM CODAIT team trained a mitosis detection model (a modified ResNet-50 model) on the TUPAC16 auxiliary mitosis dataset, and then applied it to the whole slide images for predicting the tumor proliferation scores.

This repository contains code to instantiate and deploy the mitosis detection model mentioned above. 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. For more information and additional features, check out the deep-histopath repository on GitHub.

The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Code Model Asset Exchange.

Model Metadata

Domain Application Industry Framework Training Data Input Data Format
Vision Cancer Classification Health care Keras TUPAC16 64x64 PNG Image

Note: Although this model supports different input data formats, the inference results are sensitive to the input data. In order to keep the extracted images the same as the original datasets, PNG image format should be used.

References

Licenses

Component License Link
This repository Apache 2.0 LICENSE
Training Data Custom License TUPAC16

Pre-requisites:

  • docker: The Docker command-line interface. Follow the installation instructions for your system.
  • The minimum recommended resources for this model is 2GB Memory and 2 CPUs.

Steps

  1. Deploy from Docker Hub
  2. Deploy on Kubernetes
  3. Run Locally

Deploy from Docker Hub

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 codait/max-breast-cancer-mitosis-detector

This will pull a pre-built image from Docker Hub (or use an existing image if already cached locally) and run it. If you'd rather checkout and build the model locally you can follow the run locally steps below.

Deploy on Kubernetes

You can also deploy the model on Kubernetes using the latest docker image on Docker Hub.

On your Kubernetes cluster, run the following commands:

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

The model will be available internally at port 5000, but can also be accessed externally through the NodePort.

Run Locally

  1. Build the Model
  2. Deploy the Model
  3. Use the Model
  4. Development
  5. Cleanup

1. Build the Model

Clone the MAX-Breast-Cancer-Mitosis-Detector repository locally. In a terminal, run the following command:

$ git clone https://github.com/IBM/MAX-Breast-Cancer-Mitosis-Detector.git

Change directory into the repository base folder:

$ cd MAX-Breast-Cancer-Mitosis-Detector

To build the docker image locally, run:

$ docker build -t max-breast-cancer-mitosis-detector .

All required model assets will be downloaded during the build process. Note that currently this docker image is CPU only (we will add support for GPU images later).

2. Deploy the Model

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 max-breast-cancer-mitosis-detector

3. Use the Model

The API server automatically generates an interactive Swagger documentation page. Go to http://localhost:5000 to load it. From there you can explore the API and also create test requests.

Use the model/predict endpoint to load a test image (you can use one of the test images from the assets folder) and get predicted labels for the image from the API.

Swagger Doc Screenshot

You can also test it on the command line, for example:

$ curl -F "image=@assets/true.png" -XPOST http://localhost:5000/model/predict

You should see a JSON response like that below:

{"predictions": [{"probability": 0.9884441494941711}], "status": "ok"}

4. Development

To run the Flask API app in debug mode, edit config.py to set DEBUG = True under the application settings. You will then need to rebuild the docker image (see step 1).

5. Cleanup

To stop the docker container type CTRL + C in your terminal.

Links