Abstract: A method to train a model for feature point detection involves obtaining a first image and a second image. The method involves generating a first score map for the first image and a second score map for the second image using the model. The method involves selecting a first plurality of interest points in the first image based on the first score map. The method involves selecting a second plurality of interest points in the second image based on the second score map. Pairwise matching of a first interest point among the first plurality of interest points with a second interest point among the second plurality of interest points is performed. Correctness of the pairwise matches is checked based on a ground truth, to generate a reward map. The score map and the reward map are compared and used to update the model.

Abstract: A method of predicting a progression of a condition comprises obtaining measurement data relating to at least one measurement on a subject up to a particular time point, wherein the data is generated based on an output of a sensor configured to perform the at least one measurement in respect of the subject. At least one parameter of a parameterized time-dependent function, wherein the parameterized time-dependent function is dependent on a continuous time value, is generated using a trained model and based on the measurement data, wherein the parameterized time-dependent function is indicative of a predicted progression of a condition of the subject over time after the particular time point. The parameterized time-dependent function is evaluated using the at least one parameter, for at least one time point after the particular time point.