Abstract: The image processing device 1X includes an acquisition means 30X, a variation detection means 311X, a selection means 312X, and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means 311X detects a degree of variation between the endoscopic images. The selection means 312X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means 34X detects the lesion based on a selection model that is either the first model or the second model selected.

Abstract: The image processing device 1X includes an acquisition means 30X and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means 34X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means 34X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.

Abstract: The image processing device 1X includes an acquisition means 30X, a variation detection means 311X, a selection means 312X, and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means 311X detects a degree of variation between the endoscopic images. The selection means 312X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means 34X detects the lesion based on a selection model that is either the first model or the second model selected.

Abstract: The image processing device 1X includes an acquisition means 30X and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means 34X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means 34X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.

Abstract: An information processing system includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements; a classification unit that classifies the series data into at least one class of a plurality of classes that are classification candidates, on the basis of the likelihood ratio; and a learning unit that performs learning related to calculation of the likelihood ratio, by using a loss function of a log-sum-exp type. According to such an information processing system, it is possible to properly select the class to which the series data belong, from a plurality of classes that are classification candidates.

Abstract: A motion state monitoring system, a training support system, a motion state monitoring method, and a program capable of suitably managing measurement results according to an attaching direction of a sensor are provided. A motion state monitoring system according to the present disclosure monitors a motion state of a target part of a subject's body. The motion state monitoring system includes an acquisition unit, an attaching direction detection unit, and a control processing unit. The acquisition unit acquires sensing information of a sensor attached to the target part. The attaching direction detection unit detects an attaching direction of the sensor. The control processing unit outputs information related to the sensing information in association with the attaching direction.

Abstract: An information processing apparatus includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood that the series data are derived from the same target as that of predetermined data, on the basis of at least two consecutive elements of the plurality of elements; and a determination unit that determines whether or not the series data are derived from the same target as that of the predetermined data, on the basis of the likelihood ratio. The calculation unit calculates the likelihood ratio in view of a degree of similarity or difference between the series data and the predetermined data. According to the information processing apparatus, it is possible to accurately determine whether or not the series data is derived from the same target as that of the predetermined data.

Abstract: An information processing system includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements; a classification unit that classifies the series data into at least one class of a plurality of classes that are classification candidates, on the basis of the likelihood ratio; and a learning unit that performs learning related to calculation of the likelihood ratio, by using a loss function in which the likelihood ratio increases when a correct answer class to which the series data belong is in a numerator of the likelihood ratio and the likelihood ratio decreases when the correct answer class is in a denominator of the likelihood ratio.

Abstract: An information processing system includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements: a classification unit that classifies the series data into at least one class, on the basis of the likelihood ratio; and a learning unit that performs learning related to calculation of the likelihood ratio, by using a plurality of series data. The learning unit changes a degree of contribution to the learning of each of the plurality of series data in accordance with ease of classification of the series data. According to such an information processing system, it is possible to properly perform the learning related to the calculation of the likelihood ratio.

Abstract: An information processing system (10) includes: an acquisition unit (50) configured to sequentially acquire a plurality of elements included in sequential data; a first calculation unit (110) configured to calculate, for each of the plurality of elements, a first indicator indicating which one of a plurality of classes the element belongs to; a weight calculation unit (130) configured to calculate, for each of the plurality of elements, a weight according to a confidence related to calculation of the first indicator; a second calculation unit (120) configured to calculate, based on the first indicators each weighted with the weight, a second indicator indicating which one of the plurality of classes the sequential data belongs to; and a classification unit (60) configured to classify the sequential data as any one of the plurality of classes, based on the second indicator. According to such an information processing system, sequential data can be appropriately classified.

Abstract: A learning device includes a class classification learning unit that learns class classification of a classification target by using a loss function in which a loss is calculated to become smaller as a magnitude of a difference between a function value obtained by inputting a log-likelihood ratio to a function having a finite value range and a constant associated with a correct answer to the class classification of the classification target becomes smaller, the log-likelihood ratio being the logarithm of a ratio between the likelihood that the classification target belongs to a first class and the likelihood that the classification target belongs to a second class.

Abstract: An information processing apparatus (10) includes: an acquisition unit (50) configured to sequentially acquire a plurality of elements included in sequential data; a calculation unit (100) configured to calculate, based on at least two elements of the plurality of elements, a classification indicator indicating which one of a plurality of classes the sequential data belongs to; a processing unit (200) configured to execute either a first process of resetting the classification indicator to a predetermined value, or a second process of establishing a new thread to calculate the classification indicator; and a determination unit (300) configured to determine an interval including an element of a detection-target class, based on the classification indicator. According to such an information processing apparatus, an interval including elements of the detection-target class can be appropriately determined.

Abstract: A learning apparatus includes: an identification unit that identifies a class of input data by using a learnable learning model; and an update unit that updates the learning model, by using an objective function based on relevance between a first index value for evaluating accuracy of a result of identification of the class of the input data and a second index value for evaluating time required to identify the class of the input data.