Abstract: An apparatus is provided, which includes an obtainment portion configured to obtain a plurality of images corresponding to respective values of a parameter that affects occurrence of a target event; a generation portion configured to generate, for each of a plurality of thresholds relating to the parameter, a learning data set in which at least a part of the plurality of images is each given an attribute value, the attribute value varying based on whether the threshold is exceeded; a learning processing portion configured to perform, for each generated learning data set, learning processing on an estimation model that estimates an attribute of an image in response to input of the image; and an evaluation value output portion configured to output, for each estimation model, an evaluation value according to an estimation error of the estimation model.

Abstract: In a related-art technology, training images in accordance with the number of classifications need to be prepared to perform training process. For this reason, in a case where a model is caused to output how much effect of a drug is expressed, a training image needs to be prepared for each expression degree, and it is troublesome to create the model.

Abstract: An apparatus is provided, which includes an obtainment portion configured to obtain a plurality of images corresponding to respective values of a parameter that affects occurrence of a target event; a generation portion configured to generate, for each of a plurality of thresholds relating to the parameter, a learning data set in which at least a part of the plurality of images is each given an attribute value, the attribute value varying based on whether the threshold is exceeded; a learning processing portion configured to perform, for each generated learning data set, learning processing on an estimation model that estimates an attribute of an image in response to input of the image; and an evaluation value output portion configured to output, for each estimation model, an evaluation value according to an estimation error of the estimation model.

Abstract: In a related-art technology, training images in accordance with the number of classifications need to be prepared to perform training process. For this reason, in a case where a model is caused to output how much effect of a drug is expressed, a training image needs to be prepared for each expression degree, and it is troublesome to create the model.