1. A method comprising using at least one hardware processor for: receiving a medical image; applying a mapping function to the medical image, wherein the mapping function is an SVM (Support Vector Machine) mapping function trained on a training set of medical images using a CRF (Conditional Random Field) model of the relationships between multiple received semantic descriptions of multiple medical findings in the training set; generating a semantic description of a visual finding in the medical image based on the application of the mapping function; and storing the medical image in association with the generated semantic description.
1. A method comprising using at least one hardware processor for: receiving a medical image; applying a mapping function to the medical image, wherein the mapping function is an SVM (Support Vector Machine) mapping function trained on a training set of medical images using a CRF (Conditional Random Field) model of the relationships between multiple received semantic descriptions of multiple medical findings in the training set; generating a semantic description of a visual finding in the medical image based on the application of the mapping function; and storing the medical image in association with the generated semantic description. 7. The method according to claim 1 , wherein the visual finding comprises an image feature associated with a portion of the medical image.