Abstract: Disclosed are systems and methods for classifying brain lesions based on single point in time imaging, methods for training a machine learning model for classifying brain lesions, and a method of predicting formation of brain lesions based on single point in time imaging. A method of classifying brain lesions based on single point in time imaging can include; accessing patient image data from a single point in time; providing the patient image data as an input to a brain lesion classification model; generating a classification for each of one or more lesions identified in the patient image data; and providing the classification for each of the one or more lesions for display on one or more display devices; wherein the brain lesion classification model is trained using subject image data for a plurality of subjects, the subject image data being captured at two or more points in time.

Abstract: Images are synthesized from a source to a target nature through unsupervised machine learning (ML), based on an original training set of unaligned source and target images, by training a first ML architecture through an unsupervised first learning pipeline applied to the original set, to generate a first trained model and induced target images consisting in representations of original source images compliant with the target nature. A second ML architecture is trained through a supervised second learning pipeline applied to an induced training set of aligned image pairs, each including first and second items corresponding respectively to an original source image and the induced target image associated with the latter, to generate a second trained model enabling image syntheses from the source to the target nature. Also, applications to effective medical image translations.