A computer-implemented method for identifying an object in a medical image or series of medical images, the method comprising: receiving, by one or more processors, a medical image comprising one or more objects of interest, wherein the one or more objects of interest comprise any of a tumor, an organ, or a fiducial marker; using a first neural network to identify an approximate position of an object of interest in the received image; identifying, by the one or more processors, based on the identified approximate position, a section of the received image that includes the object of interest; obtaining, by the one or more processors, a plurality of sub-images corresponding to different sub-sections of the identified section of the received image; applying the plurality of sub-images to a plurality of second neural networks, each of the second neural networks determining a respective position of the object of interest, such that the plurality of second neural networks determine a respective plurality of secondary positions of the object of interest; and determining the average position of the object based on the secondary positions of the object determined by the plurality of second neural networks, wherein the determined average position is determined on the basis of an average position of the secondary positions excluding positions which deviate more than one standard deviation from a mean position among the secondary positions, wherein prior to receiving the medical image comprising one or more objects of interest, the method includes: training, by one or more processors, the first neural network to learn to identify the approximate position of the object of interest in a medical training image; and training, by one or more processors, the plurality of second neural networks to learn to identify the position of the object of interest in the medical training image. receiving, by one or more processors, a medical image comprising one or more objects of interest, wherein the one or more objects of interest comprise any of a tumor, an organ, or a fiducial marker; using a first neural network to identify an approximate position of an object of interest in the received image; identifying, by the one or more processors, based on the identified approximate position, a section of the received image that includes the object of interest; obtaining, by the one or more processors, a plurality of sub-images corresponding to different sub-sections of the identified section of the received image; applying the plurality of sub-images to a plurality of second neural networks, each of the second neural networks determining a respective position of the object of interest, such that the plurality of second neural networks determine a respective plurality of secondary positions of the object of interest; and determining the average position of the object based on the secondary positions of the object determined by the plurality of second neural networks, wherein the determined average position is determined on the basis of an average position of the secondary positions excluding positions which deviate more than one standard deviation from a mean position among the secondary positions, wherein prior to receiving the medical image comprising one or more objects of interest, the method includes: training, by one or more processors, the first neural network to learn to identify the approximate position of the object of interest in a medical training image; and training, by one or more processors, the plurality of second neural networks to learn to identify the position of the object of interest in the medical training image.