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 traveling platform includes a side arm, a first guide, and two or more travel auxiliary rollers. The side arm expands and retracts in a Y direction. The first guide extends in an X direction and includes a third inside surface to guide the side arm in the X direction. The two or more travel auxiliary rollers are provided to the first guide and restrict movement of the traveling platform in the Y direction.

Abstract: A transport vehicle includes a pair of top arms that extend and retract, a pair of guides disposed below the pair of top arms, an adjuster that changes a spacing between the pair of guides, a cargo placement surface disposed below the pair of guides, a clamping detector that detects whether or not the cargo is being held between the pair of guides, and a controller. The controller pulls the cargo onto the cargo placement surface, then causes the adjuster to start a positioning operation as an operation to reduce a spacing between the pair of guides, and stops the positioning operation when the clamping detector detects that the cargo is being held between the pair of guides.

Abstract: A transport vehicle includes a pair of top arms that extend and retract, a pair of guides disposed below the pair of top arms, an adjuster that changes a spacing between the pair of guides, a cargo placement surface disposed below the pair of guides, a clamping detector that detects whether or not the cargo is being held between the pair of guides, and a controller. The controller pulls the cargo onto the cargo placement surface, then causes the adjuster to start a positioning operation as an operation to reduce a spacing between the pair of guides, and stops the positioning operation when the clamping detector detects that the cargo is being held between the pair of guides.