1. An information processing device comprising: an obtaining section that obtains an output value given in 5 response to inputting a target image into a classification model generated by carrying out learning so that distances between feature quantities extracted from a first image group having a common feature become small when the feature quantities are embedded in a feature space; and a determining section that applies, on a basis of the output value, 10 a first method for the first image group or a second method for a second image group, which is constituted by an image not belonging to the first image group, to determine a given determination matter relating to the target image. 15 2. The information processing device as claimed in claim 1, wherein: the first image group is an image group for which determination on a basis of an output value given in response to inputting the target image into a learned model generated by machine learning is effective; the first method includes at least a process of determining the 20 determination matter with use of the learned model; and the second method includes at least a process of determining the determination matter through numerical analysis of pixel values in the target image. 25 3. The information processing device as claimed in claim 1, wherein: the first image group is an image group for which determination on a basis of an output value given in response to inputting the target 73 image into a learned model generated by machine learning is effective; the first method is a method of determining the determination matter with use of a plurality of methods and then integrating results of the determinations to carry out final determination; 5 the plurality of methods include a method of determining the determination matter with use of the learned model and a method of determining the determination matter on a basis of the output value from the classification model; and 10 the second method is a method of determining the determination matter through numerical analysis of pixel values in the target image.

4. The information processing device as claimed in claim 1, wherein: the given determination matter is presence or absence of an 15 abnormal portion in a target in the target image; an image included in the first image group is an image of a target which does not include a pseudo abnormal portion similar in appearance to the abnormal portion; an image included in the second image group is an image of a 20 target including the pseudo abnormal portion; the output value from the classification model indicates whether the target image belongs to the second image group, the target image is an image belonging to the first image group and including the abnormal portion, or the target image is an image belonging to the first image group 25 and not including the abnormal portion; the first method includes at least a process of determining presence or absence of the abnormal portion in the target on a basis of the 74 output value; and the second method includes at least a process of determining presence or absence of the abnormal portion in the target through numerical analysis of pixel values in the target image. 5

5. The information processing device as claimed in claim 1, wherein: the first image group is an image group for which determination on a basis of an output value given in response to inputting the target image into a learned model generated by machine learning is effective; 10 the determination section determines the determination matter by a plurality of methods and then synthesizes results of the determinations to determine the determination matter; the plurality of methods include a method of determining the determination matter with use of the learned model and a method of 15 determining the determination matter through numerical analysis of pixel values in the target image; the information processing device further comprises a weight setting section that sets weights on the results of the determinations, the weights being used in integration of the results of the determinations; 20 in a case where the first method is applied, the weight setting section sets a weight on the result of the determination given by the method involving use of the learned model so as to be equal to or heavier than a weight on the result of the determination given by the method carrying out the numerical analysis; and 25 in a case where the second method is applied, the weight setting sections sets a weight on the result of the determination given by the method carrying out the numerical analysis so as to be heavier than a 75 weight on the result of the determination given by the method involving use of the learned model.

6. The information processing device as claimed in claim 5, further 5 comprising: a reliability determining section that carries out, for each of the plurality of methods, a process of determining, on a basis of the target image, a reliability which is an indicator indicating a degree of certainty of the result of the determination, wherein 10 the determining section determines the determination matter with use of the results of the determinations, the reliabilities determined by the reliability determining section, and the weights set by the weight setting section. 15 7. A determination method executed by an information processing device, comprising: an obtaining step of obtaining an output value given in response to inputting a target image into a classification model generated by carrying out learning so that distances between feature quantities 20 extracted from a first image group having a common feature become small when the feature quantities are embedded in a feature space; and a determination step of applying, on a basis of the output value, a first method for the first image group or a second method for a second image group, which is constituted by an image not belonging to the first 25 image group, to determine a given determination matter relating to the target image. 76

8. A determination program causing a computer to function as an information processing device recited in claim 1, the determination program causing the computer to function as the obtaining section and the determining section

We Claim:
1. An information processing device comprising:
an obtaining section that obtains an output value given in
5 response to inputting a target image into a classification model generated
by carrying out learning so that distances between feature quantities
extracted from a first image group having a common feature become small
when the feature quantities are embedded in a feature space; and
a determining section that applies, on a basis of the output value,
10 a first method for the first image group or a second method for a second
image group, which is constituted by an image not belonging to the first
image group, to determine a given determination matter relating to the
target image.
15 2. The information processing device as claimed in claim 1, wherein:
the first image group is an image group for which determination
on a basis of an output value given in response to inputting the target
image into a learned model generated by machine learning is effective;
the first method includes at least a process of determining the
20 determination matter with use of the learned model; and
the second method includes at least a process of determining the
determination matter through numerical analysis of pixel values in the
target image.
25 3. The information processing device as claimed in claim 1, wherein:
the first image group is an image group for which determination
on a basis of an output value given in response to inputting the target
73
image into a learned model generated by machine learning is effective;
the first method is a method of determining the determination
matter with use of a plurality of methods and then integrating results of
the determinations to carry out final determination;
5 the plurality of methods include
a method of determining the determination matter with
use of the learned model and
a method of determining the determination matter on a
basis of the output value from the classification model; and
10 the second method is a method of determining the determination
matter through numerical analysis of pixel values in the target image.
4. The information processing device as claimed in claim 1, wherein:
the given determination matter is presence or absence of an
15 abnormal portion in a target in the target image;
an image included in the first image group is an image of a target
which does not include a pseudo abnormal portion similar in appearance
to the abnormal portion;
an image included in the second image group is an image of a
20 target including the pseudo abnormal portion;
the output value from the classification model indicates whether
the target image belongs to the second image group, the target image is an
image belonging to the first image group and including the abnormal
portion, or the target image is an image belonging to the first image group
25 and not including the abnormal portion;
the first method includes at least a process of determining
presence or absence of the abnormal portion in the target on a basis of the
74
output value; and
the second method includes at least a process of determining
presence or absence of the abnormal portion in the target through
numerical analysis of pixel values in the target image.
5
5. The information processing device as claimed in claim 1, wherein:
the first image group is an image group for which determination
on a basis of an output value given in response to inputting the target
image into a learned model generated by machine learning is effective;
10 the determination section determines the determination matter by
a plurality of methods and then synthesizes results of the determinations
to determine the determination matter;
the plurality of methods include a method of determining the
determination matter with use of the learned model and a method of
15 determining the determination matter through numerical analysis of pixel
values in the target image;
the information processing device further comprises a weight
setting section that sets weights on the results of the determinations, the
weights being used in integration of the results of the determinations;
20 in a case where the first method is applied, the weight setting
section sets a weight on the result of the determination given by the
method involving use of the learned model so as to be equal to or heavier
than a weight on the result of the determination given by the method
carrying out the numerical analysis; and
25 in a case where the second method is applied, the weight setting
sections sets a weight on the result of the determination given by the
method carrying out the numerical analysis so as to be heavier than a
75
weight on the result of the determination given by the method involving
use of the learned model.
6. The information processing device as claimed in claim 5, further
5 comprising:
a reliability determining section that carries out, for each of the
plurality of methods, a process of determining, on a basis of the target
image, a reliability which is an indicator indicating a degree of certainty of
the result of the determination, wherein
10 the determining section determines the determination matter with
use of the results of the determinations, the reliabilities determined by the
reliability determining section, and the weights set by the weight setting
section.
15 7. A determination method executed by an information processing
device, comprising:
an obtaining step of obtaining an output value given in response
to inputting a target image into a classification model generated by
carrying out learning so that distances between feature quantities
20 extracted from a first image group having a common feature become small
when the feature quantities are embedded in a feature space; and
a determination step of applying, on a basis of the output value, a
first method for the first image group or a second method for a second
image group, which is constituted by an image not belonging to the first
25 image group, to determine a given determination matter relating to the
target image.
76
8. A determination program causing a computer to function as an
information processing device recited in claim 1, the determination
program causing the computer to function as the obtaining section and
the determining section