Abstract: Disclosed systems and methods provide a framework for evaluating AI systems without ground-truth annotations. The disclosed embodiments may assign temporary labels to data points in sets of working data and use the temporarily labeled data to train one or more distinct models. These models may be evaluated to determine which has the highest performance and is thus indicative of the temporary labels most likely to be correct.

Abstract: Methods and systems are described for image segmentation. A machine learning model is applied to a set of images to generate results. The results may be obtained as a probability map for each image in the set of images. The model may be trained by accessing a set of labeled images, each image associated with a label indicating a location of a feature within a respective image. An initial set of parameters is accessed. An encoder is initialized with the initial set of parameters. The encoder is applied to the set of labeled images to generate a prediction of a feature location within each image. The initial set of parameters are updated based on the predictions and the label associated with the labeled images. The updated set of parameters and an additional set of parameters generated using a set of unlabeled images are aggregated.

Abstract: Methods and systems are described for image segmentation. A machine learning model is applied to a set of images to generate results. The results may be obtained as a probability map for each image in the set of images. The model may be trained by accessing a set of labeled images, each image associated with a label indicating a location of a feature within a respective image. An initial set of parameters is accessed. An encoder is initialized with the initial set of parameters. The encoder is applied to the set of labeled images to generate a prediction of a feature location within each image. The initial set of parameters are updated based on the predictions and the label associated with the labeled images. The updated set of parameters and an additional set of parameters generated using a set of unlabeled images are aggregated.