1. A method for a cloud-based feedback-driven image training and recognition, comprising the steps of: receiving a set of expert annotations of a plurality of training images of a predetermined subject matter, wherein the expert annotations include a clinical diagnosis for each image or region of interest in an image; training one or more classification models from the set of expert annotations; testing the one or more classification models on a plurality of test images that are different from the training images, wherein each classification model yields a clinical diagnosis for each image and a confidence score for that diagnosis; and receiving expert classification result feedback regarding the clinical diagnosis for each image and a confidence score yielded by each classification model, wherein the expert classification result feedback comprises receiving a re-annotated classified image, wherein the classification model is retrained using the re-annotated classified image, and wherein the steps of training one or more classification models and testing the one or more classification models on a plurality of test images are performed on a public cloud service platform.