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. <EOS>