Meta-learning for cardiac MRI segmentation

A non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to:

• apply a machine learning model to a set of images to generate results, wherein the machine learning model was trained by:
• initializing a first encoder with a first set of parameters,
• applying the first encoder to a set of unlabeled images to generate a set of representations of the unlabeled images,
• updating the first set of parameters based on the set of representations,
• initializing a second encoder with a second set of parameters,
• applying the second encoder to a set of labeled images to generate a set of predictions of a location of a feature within each image in the set of labeled images, wherein each respective label is associated with the location of the feature in the respective labeled image,
• updating the second set of parameters based on the set of predictions,
• aggregating the updated first set of parameters and the updated second set of parameters into a set of aggregated parameters, and
• updating the first set of parameters based on the set of aggregated parameters;
• and
• obtain the results of the machine learning model as a probability map for each image in the set of images.