Abstract: A framework for a few-shot learning method is disclosed. In a first part, self-supervision and classification supervision are used to train a feature extractor. An example self-supervision method comprises running grayscale images through an edge filter, normalizing the filtered images, setting the normalized images to ground truth, generating feature-extracted images, using a decoder to reconstruct images from the feature-extracted images, determining a loss between the reconstructed images and the ground truth images, and using the loss to update parameters of the feature extractor. In a second part, a few-shot adaptation process is performed to adapt the model to a novel rapid test kit.