Method and system for training a neural network

A method for training a neural network for detecting a plurality of classes of object within a sample, the method comprising:

• providing a training data set comprising a plurality of samples, each annotated according to whether the samples include labelled objects of interest, where;
• in a first type of samples, all objects of interest are labelled according to their class and comprise a foreground of said samples, the remainder of said samples comprising background;
• in a second type of samples, some objects of interest are labelled in a foreground and their background may comprise unlabelled objects;
• and
• a third type of samples comprise only background comprising no objects of interest;
• for any given training epoch:
• selecting a number of samples of each of said first, second and third type;
• applying a given generation of said neural network to said selected samples to produce a respective set of one or more feature maps for each sample;
• for portions of said feature maps corresponding to locations of objects labelled as being objects of a given class from said first type of samples, performing a positive loss function to determine a difference between at least one feature map value and a positive value associated with foreground;
• for portions of said feature maps not corresponding to locations of objects labelled as being objects of a given class from said first type of samples, performing a negative loss function to determine a difference between at least feature map value and a negative value associated with background;
• for portions of said feature maps corresponding to locations of labelled objects from said second type of samples, only performing a loss function to determine a difference between at least one feature map value and a value associated with foreground;
• for limited portions of said feature maps of said third type of samples, performing a negative loss function to determine a difference between at least feature map value and a negative value associated with background;
• and
• combining said loss function results before updating the weights for a subsequent generation of said neural network.