Apparatus, information processing method, and computer-readable recording medium for disease correlation and inferencing

A method comprising:

• acquiring a medical image;
• extracting one or more image feature values from the medical image;
• inferring one or more diseases predicted from the medical image, based on the one or more image feature values and a classification basis, the classification basis being a set of C basis vectors with each basis vector representing correlations between a disease of a set of C diseases and other diseases by using a vector of a certain dimension, wherein C is an integer of 2 or more;
• and
• outputting inferred results.

A non-transitory computer-readable recording medium recording a program that causes one or more computers to perform the processes of:

• acquiring a medical image;
• extracting one or more image feature values from the medical image;
• inferring one or more diseases predicted from the medical image, based on the one or more image feature values and a classification basis, the classification basis being a set of C basis vectors with each basis vector representing correlations between a disease of a set of C diseases and other diseases by using a vector of a certain dimension, wherein C is an integer of 2 or more;
• and
• outputting inferred results.

An information processing apparatus comprising:

• a processor configured to:
• generate a first machine learning model that outputs one or more image feature values from an image, the image feature value representing a feature of the image;
• generate a second machine learning model that outputs a classification basis that has learned correlations among diseases from input data generated based on label information about a plurality of medical images labeled with one or more diseases;
• cause the first machine learning model and the second machine learning model to do learning using the plurality of medical images labeled with one or more diseases, find certainty factors of the respective diseases based on the one or more image feature values acquired from the first machine learning model and the classification basis acquired from the second machine learning model, and cause the first machine learning model and the second machine learning model to do learning so as to minimize differences between the certainty factors and the diseases labeled to the medical images, the classification basis being a set of C basis vectors with each basis vector representing correlations between a disease of a set of C diseases and other diseases by using a vector of a certain dimension, wherein C is an integer of 2 or more;
• output a learned model obtained through learning done by the first machine learning model;
• and
• output the classification basis obtained through learning done by the second machine learning model.

A method comprising:

• acquiring a plurality of medical images labeled with one or more diseases and a plurality of medical images free of diseases;
• causing a first machine learning model and a second machine learning model to do learning using the medical images, the first machine learning model being a machine learning model that outputs one or more image feature values from an image, the image feature value representing a feature of the image, the second machine learning model being a machine learning model that outputs a classification basis that has learned correlations among diseases from input data generated based on label information about the plurality of medical images labeled with one or more diseases, finding certainty factors of the respective diseases based on the one or more image feature values acquired from the first machine learning model and the classification basis acquired from the second machine learning model, and causing the first machine learning model and the second machine learning model to do learning so as to minimize differences between the certainty factors and the diseases labeled to the medical images, the classification basis being a set of C basis vectors with each basis vector representing correlations between a disease of a set of C diseases and other diseases by using a vector of a certain dimension, wherein C is an integer of 2 or more;
• outputting a learned model obtained through learning done by the first machine learning model;
• and
• outputting the classification basis obtained through learning done by the second machine learning model.