Abstract: A learning model generates a plurality of prototype vectors and generates an integrated similarity vector that indicates similarity between an input image and each prototype for a plurality of prototypes in accordance with similarity between one prototype vector and each pixel vector in a feature map acquired from an CNN. An image processing apparatus obtains prototype belongingness (distributed prototype belongingness) for each image by distributing prototype belongingness of a belonged prototype of each class to each of two or more images that belong to one class. Then, the learning model is subjected to machine learning in accordance with the distributed prototype belongingness of each prototype vector for each image so that each prototype vector is brought closer to any pixel vector in the feature map corresponding to each image.

Abstract: A training method includes: generating a first image by adding noise to a first area; generating a second image by adding noise to a second area; generating a combined image by weighted addition of the first image and the second image; generating a first training label for the first image; generating a second training label for the second image; generating a combined training label by weighted addition of the first training label and the second training label; and generating a learning model by machine learning using the combined image and the combined training label.

Abstract: An information processing method includes processing of: acquiring, from a plurality of time-series images in which an object is captured, first information including at least a plurality of positions or a plurality of sizes of the object; executing prediction processing of predicting second information including at least one of a position or a size of the object at a next time point in a time-series based on the first information and recursively executing the prediction processing based on the first information and the second information to predict the second information of the object at a time point further next to the next time point; executing recognition processing of recognizing motion of the object based on the second information; and determining a total number of times of recursion of the prediction processing based on a result of the recognition processing.

Abstract: An information processing method includes processing of: acquiring, from a plurality of time-series images in which an object is captured, first information including at least a plurality of positions or a plurality of sizes of the object; executing prediction processing of predicting second information including at least one of a position or a size of the object at a next time point in a time-series based on the first information and recursively executing the prediction processing based on the first information and the second information to predict the second information of the object at a time point further next to the next time point; executing recognition processing of recognizing motion of the object based on the second information; and determining a total number of times of recursion of the prediction processing based on a result of the recognition processing.

Abstract: An endoscopic image diagnosis support system (100) includes: a memory (10) that stores learning images pre-classified into pathological types; and a processor (20) that, given an endoscopic image, performs feature value matching between an image of an identification target region in the endoscopic image and the learning images, to identify the pathological types in the identification target region.

Abstract: An endoscopic image diagnosis support system (100) includes: a memory (10) that stores learning images pre-classified into pathological types; and a processor (20) that, given an endoscopic image, performs feature value matching between an image of an identification target region in the endoscopic image and the learning images, to identify the pathological types in the identification target region.