Learning apparatus and learning method for training neural network

A learning apparatus comprising:

• a memory that stores a first image set in which a first image for learning and first mask data for identifying a region larger than a region of interest included in the first image are associated with each other and a second image set in which a second image for learning and second mask data for identifying a region of interest included in the second image on a per pixel basis or on a per block-of-pixels basis are associated with each other;
• and
• a processor, configured to:
• input the first image to a neural network and obtain a first feature map output from the neural network;
• compare the first feature map with the first mask data associated with the first image to calculate a first loss value;
• update a plurality of parameters of the neural network on the basis of the first loss value;
• obtain the second image and the second mask data from the image set;
• input the second image to the neural network and obtain a second feature map output from the neural network;
• compare the second feature map with the second mask data associated with the second image to calculate a second loss value;
• and
• update the plurality of parameters of the neural network on the basis of the second loss value.

A learning method comprising:

• preparing a first image set in which a first image for learning and coordinate information for identifying a region larger than a region of interest included in the first image are associated with each other and a second image set in which a second image for learning and second mask data for identifying a region of interest included in the second image on a per pixel basis or on a per block-of-pixels basis are associated with each other;
• inputting the first image to a neural network and obtaining a first feature map output from the neural network;
• comparing the first feature map with the coordinate information associated with the first image to calculate a first loss value;
• updating a plurality of parameters of the neural network on the basis of the first loss value;
• obtaining the second image and the second mask data from the second image set;
• inputting the second image to the neural network and obtaining a second feature map output from the neural network;
• comparing the second feature map with the second mask data associated with the second image to calculate a second loss value;
• and
• updating the plurality of parameters of the neural network on the basis of the second loss value.

A learning method comprising:

• preparing a first image set in which a first image for learning and first mask data for identifying a region larger than a region of interest included in the first image are associated with each other and a second image set in which a second image for learning and second mask data for identifying a region of interest included in the second image on a per pixel basis or on a per block-of-pixels basis are associated with each other;
• inputting the first image to a neural network and obtaining a first feature map output from the neural network;
• comparing the first feature map with the first mask data associated with the first image to calculate a first loss value;
• updating a plurality of parameters of the neural network on the basis of the first loss value;
• obtaining the second image and the second mask data from the image set;
• updating a plurality of parameters of the neural network on the basis of the first loss value;
• obtaining the second image and the second mask data from the image set;
• inputting the second image to the neural network and obtaining a second feature map output from the neural network;
• comparing the second feature map with the second mask data associated with the second image to calculate a second loss value;
• and
• updating the plurality of parameters of the neural network on the basis of the second loss value.