The Scancer API is primarily designed to be used as part of the webapp that is hosted on scancer.org. In order to enable its use in your own applications, we also expose the API to the web at https://api.scancer.org/. We have locked this behind authentication to maintain some control of access to it, but please write to us if you would like to use it. Send us a short description of your intended usage and we will be happy to get you setup with credentials.

Once you have credentials (username, password), you can perform inference on medical images to aid in cancer detection in the following way:

1. Browse the list of model repositories under the Scancer GitHub Organisation and find the one most suitable to your requirements. These are consistently named ml-<dataset>-<task>, e.g. ml-pcam-classificationfor identifying metastatic cancer in small image patches taken from larger digital pathology scans trained on the PatchCamelyon dataset.
2. Now that you have the model you want to work with, browse the repository to find more information about the format of the input images. The model repository will also contain test images for you to experiment with the API.
3. The endpoint you need to access for this model is systematically named https://api.scancer.org/predictions/<dataset>-<task>, and it always contains our best performing model for this task. You can perform inference with it by PUTting an image to it, as in the following example:

   curl --user <username>:<password> https://api.scancer.org/predictions/pcam-classification -T pcam-example.png
   

   The server will respond with a response that corresponds to the task at hand. e.g. In this case, it will return 0 (no metastatic cancer) or 1 (presence of metastatic cancer somewhere in the input image).