Computer-readable recording medium having stored therein training program, training method, and information processing apparatus

A training method executed by a computer, the training method comprising:

• acquiring training data including moving image data obtained by photographing a target and a plurality of annotation images each indicative of a region of the target in each of a plurality of frame images included in the moving image data;
• and
• executing a training process using the training data, wherein
• the training process comprises:
• detecting the target included in the plurality of frame images;
• inputting a combined image to an auto-encoder, the combined image being obtained by combining a plurality of partial images including the target and a plurality of peripheral region images of the target, the plurality of partial images and plurality of peripheral region images being detected in a given number of preceding and succeeding second frame images in a time series of the moving image data of a first frame image from among the plurality of frame images;
• inputting a partial image, in the plurality of partial images, corresponding to the first frame image to a neural network that performs a segmentation process for an image;
• and
• performing parameter update of the auto-encoder and the neural network, based on a difference between a combination output image obtained by combining an output image from the auto-encoder and an output image from the neural network and a partial image of the annotation image indicative of a region of the target in the first frame image.

An information processing apparatus, comprising:

• a memory;
• and
• a processor coupled to the memory, the processor being configured to:
• acquire training data including moving image data obtained by photographing a target and a plurality of annotation images indicative of a region of the target in each of a plurality of frame images included in the moving image data;
• and
• execute a training process of an auto-encoder and a neural network, which performs a segmentation process for an image, using the training data, wherein
• the processor performs, in the training process:
• detecting the target included in the plurality of frame images;
• inputting a combined image to an auto-encoder, the combined image being obtained by combining a plurality of partial images including the target and a plurality of peripheral region images of the target, the plurality of partial images and plurality of peripheral region images being detected in a given number of preceding and succeeding second frame images in a time series of the moving image data of a first frame image from among the plurality of frame images;
• inputting a partial image, in the plurality of partial images, corresponding to the first frame image to the neural network;
• and
• performing parameter update of the auto-encoder and the neural network, based on a difference between a combination output image obtained by combining an output image from the auto-encoder and an output image from the neural network and a partial image of the annotation image indicative of a region of the target in the first frame image.