Abstract: A method includes: acquiring data including moving image obtained by photographing a target and annotation images each indicative of a region of the target in each of frame images in the moving image; executing a process using the data. The process includes: detecting the target in the frame images; inputting, to an auto-encoder, an image obtained by combining partial images including the target and peripheral region images of the target detected in a given number of preceding and succeeding second frame images in a time series of the moving image of a first frame image; inputting a partial image corresponding to the first frame image to a neural network performing a segmentation; updating parameter of the auto-encoder and the neural network based on a difference between an image obtained by combining images from the auto-encoder and the neural network and a partial image of the annotation image.

Abstract: A method includes: acquiring a training data set including pieces of training data, each of the pieces including an image of a training target, first annotation data representing a rectangular region in the image, and second annotation data; training, based on the image and the first annotation data, an object detection model specifying a rectangular region including the training target; training, based on the image and the second annotation data, a neural network; and calculating a first index value related to a relationship of a pixel number, the trained estimation model and the calculated first index value being used in a determination process that determines, based on the calculated first index value and a second index value relationship between a pixel number in an output result and an estimation result, whether or not a target in a target image is normal.

Abstract: A method includes: acquiring data including moving image obtained by photographing a target and annotation images each indicative of a region of the target in each of frame images in the moving image; executing a process using the data. The process includes: detecting the target in the frame images; inputting, to an auto-encoder, an image obtained by combining partial images including the target and peripheral region images of the target detected in a given number of preceding and succeeding second frame images in a time series of the moving image of a first frame image; inputting a partial image corresponding to the first frame image to a neural network performing a segmentation; updating parameter of the auto-encoder and the neural network based on a difference between an image obtained by combining images from the auto-encoder and the neural network and a partial image of the annotation image.

Abstract: A method includes: acquiring a training data set including pieces of training data, each of the pieces including an image of a training target, first annotation data representing a rectangular region in the image, and second annotation data; training, based on the image and the first annotation data, an object detection model specifying a rectangular region including the training target; training, based on the image and the second annotation data, a neural network; and calculating a first index value related to a relationship of a pixel number, the trained estimation model and the calculated first index value being used in a determination process that determines, based on the calculated first index value and a second index value relationship between a pixel number in an output result and an estimation result, whether or not a target in a target image is normal.