Abstract: An object is to provide an endoscopic image learning device, an endoscopic image learning method, an endoscopic image learning program, and an endoscopic image recognition device that appropriately learn a learning model for image recognition for recognizing an endoscopic image in which a treatment tool for an endoscope appears. The object is achieved by an endoscopic image learning device including an image generation unit and a machine learning unit. The image generation unit generates a superimposed image where a foreground image in which a treatment tool for an endoscope is extracted is superimposed on a background-endoscopic image serving as a background of the foreground image, and the machine learning unit performs the learning of a learning model for image recognition using the superimposed image.

Abstract: An endoscope image processing apparatus includes: an image acquisition unit that acquires endoscopic images; a detection unit that detects lesion images representing lesions in the endoscopic images acquired by the image acquisition unit; a clustering unit that groups the endoscopic images on the basis of a degree of correlation between the lesion images and generates, for each lesion, a group formed of corresponding ones of the endoscopic images; a representative image selection unit that selects, for each group, a representative image from among the endoscopic images in the group; a saving unit that saves, for each group, the representative image and the endoscopic images that form the group to which the representative image belongs, in association with each other; and a display unit that displays a list of the representative images saved in the saving unit.

Abstract: An endoscope apparatus includes: a lesion information acquisition unit that acquires acquired lesion information; a lesion identification unit that compares existing lesion information with the acquired lesion information and determines whether the acquired lesion information corresponds to the same lesion indicated by the existing lesion information; a lesion count unit that counts a lesion count, which is the number of lesions detected in a single endoscopic examination; a display unit; and a control unit that increments the lesion count counted by the lesion count unit by one when the lesion identification unit determines that the acquired lesion information does not correspond to the same lesion indicated by the existing lesion information.

Abstract: The present disclosure allows information on an object included in a moving image to be superimposed on the moving image and displayed with high visibility. Position information and type information of an object included in frame images of an acquired moving image are acquired, a first display position at which the position information is to be displayed and a second display position at which the type information is to be displayed are determined, the sequentially acquired frame images are sequentially displayed on a display, the position information and the type information are displayed so as to be superimposed on each of the displayed frame images at the first display position and the second display position, the first display position is updated with a first update frequency, and the second display position is updated with a second update frequency lower than the first update frequency.

Abstract: A medical information processing apparatus according to an aspect of the present invention is a medical information processing apparatus including a processor. The processor is configured to execute a spatial information acquisition process of sequentially acquiring pieces of spatial information of a lumen on the basis of endoscopic images sequentially acquired by an endoscope; an estimation process of estimating, on the basis of the pieces of spatial information sequentially acquired, at least one of a three-dimensional environment map of the lumen or a distal end position of the endoscope; a reference information acquisition process of acquiring reference information about a shape and/or an absolute size of the lumen; and a correction process of correcting, using the reference information, one or more of the spatial information, the three-dimensional environment map, and the distal end position of the endoscope.

Abstract: A region of interest located in a first part to be observed is detected from a medical image at a first timing. In a case where a tip portion of an endoscope moves from the first part to be observed toward a second part to be observed and then moves to the first part to be observed again, a return determination unit performs first determination processing for determining whether the tip portion has returned to the first part to be observed, by using a medical image acquired a second timing. A display control unit performs display control of a monitor by using a determination result of the return determination unit.

Abstract: A medical image processing apparatus includes a processor configured to execute an image acquisition process for sequentially acquiring time-series medical images; a region-of-interest recognition process for recognizing a position and a type of a region of interest from the medical images; and a display control process for causing a display device to display position information indicating the position of the region of interest and type information indicating the type of the region of interest such that the position information and the type information are superimposed on the medical images. In the display control process, the processor changes a position at which the position information is to be displayed according to a change in the position of the region of interest over time, and maintains a position at which the type information is to be displayed regardless of a change in the position of the region of interest over time.

Abstract: There are provided a machine learning system, a learning method, and a program that can facilitate learning of a large number of combined medical images. A machine learning system (10) includes: an image database (14) that stores a plurality of medical images; a geometric-shape database (16) that stores geometric shapes to be superimposed on the medical images; a processor (22); and a learning model (30), in which the processor (22) is configured to perform a selection accepting process of accepting selection of a medical image from among the medical images stored in the image database (14) and selection of a geometric shape from among the geometric shapes stored in the geometric-shape database (16), a geometric-shape combining process of combining the selected medical image and the selected geometric shape and generating a composite image, and a training process of making the learning model perform learning by using the composite image.

Abstract: Provided are an image processing apparatus, method, and program that can appropriately support a treatment using an instrument. An image processing apparatus for processing an image captured with an endoscope having a distal end from which an instrument is protrudable includes a processor. The processor is configured to perform a process of acquiring an image captured with the endoscope, a process of causing a display to display the acquired image, a process of detecting a region of interest from the acquired image, a process of determining whether the region of interest is present at a position reachable by the instrument protruded from the distal end of the endoscope, and a process of providing a notification in a case where it is determined that the region of interest is present at a position reachable by the instrument protruded from the distal end of the endoscope.

Abstract: There are provided an endoscopic image processing device, an endoscopic image processing method, and an endoscope system that appropriately notify region-of-interest information about a region of interest included in an endoscopic image according to an endoscope operator's action. An endoscope operator's action on a portion to be observed of an endoscopic image is recognized from endoscopic images, and first emphasis display where region-of-interest information is displayed in the endoscopic image at a first emphasis level and second emphasis display where the region-of-interest information is displayed at a second emphasis level relatively lower than the first emphasis level are switched according to a recognition result.

Abstract: There are provided an endoscopic image processing device, an endoscopic image processing method, an endoscopic image processing program, and an endoscope system that appropriately notify region-of-interest information about a region of interest included in an endoscopic image according to an endoscope operator's action. An endoscope operator's action on a portion to be observed of an endoscopic image is recognized from endoscopic images, and first emphasis display where region-of-interest information is displayed in the endoscopic image at a first emphasis level and second emphasis display where the region-of-interest information is displayed at a second emphasis level relatively lower than the first emphasis level are switched according to a recognition result.

Abstract: An information processing apparatus detects, by a first detector, a region-of-interest reflected in medical image data, performs a control of displaying, on a display, the medical image data which is a detection result indicating that the region-of-interest is not detected, receives information for specifying the medical image data evaluated that the detection result is false negative by a user, re-detects, by a second detector, the region-of-interest based on the false negative medical image data, and trains the first detector using a first medical image data set including the medical image data and a re-detection result.

Abstract: An object is to provide an endoscopic image learning device, an endoscopic image learning method, an endoscopic image learning program, and an endoscopic image recognition device that appropriately learn a learning model for image recognition for recognizing an endoscopic image in which a treatment tool for an endoscope appears. The object is achieved by an endoscopic image learning device including an image generation unit and a machine learning unit. The image generation unit generates a superimposed image where a foreground image in which a treatment tool for an endoscope is extracted is superimposed on a background-endoscopic image serving as a background of the foreground image, and the machine learning unit performs the learning of a learning model for image recognition using the superimposed image.

Abstract: Provided are a medical image processing apparatus, a medical image processing method, a program, and a diagnosis support apparatus that report a region of interest without hindering observation of the boundary between the region of interest and a region of non-interest in a medical image. A medical image processing apparatus includes a superimposition processing unit that superimposes, on a medical image, a figure for reporting a region of interest included in the medical image. The superimposition processing unit superimposes the figure on an inside of the region of interest such that at least part of a boundary between the region of interest and a region of non-interest is not superimposed with the figure, thereby reporting the region of interest without hindering observation of the boundary between the region of interest and the region of non-interest.

Abstract: An object is to provide an endoscopic image learning device, an endoscopic image learning method, an endoscopic image learning program, and an endoscopic image recognition device that appropriately learn a learning model for image recognition for recognizing an endoscopic image in which a treatment tool for an endoscope appears. The object is achieved by an endoscopic image learning device including an image generation unit and a machine learning unit. The image generation unit generates a superimposed image where a foreground image in which a treatment tool for an endoscope is extracted is superimposed on a background-endoscopic image serving as a background of the foreground image, and the machine learning unit performs the learning of a learning model for image recognition using the superimposed image.

Abstract: An object of the present invention is to provide an image diagnosis assistance apparatus, an endoscope system, and an image diagnosis assistance method that are capable of appropriately performing reporting by using screen display and audio. An image diagnosis assistance apparatus according to a first aspect of the present invention includes an image acquiring unit that acquires a chronological medical image, a recognizing unit that performs recognition of a region of interest in the acquired medical image, a reporting unit that performs reporting of a result of the recognition by using screen display and audio, and a determining unit that makes a determination on an examination status.

Abstract: An endoscope device, an endoscope operation method, and a program are provided. An endoscope device includes a motion pattern identification unit that identifies a single or plurality of motion patterns indicating a specific movement of a scope head of a scope unit, an operation storage unit that stores a single or plurality of pieces of operation information for instructing an operation of the endoscope device, and the single or plurality of motion patterns of the scope head in association with each other, an operation information acquisition unit that acquires the operation information corresponding to the motion pattern from the operation storage unit on the basis of the motion pattern identified by the motion pattern identification unit, and an operation execution unit that executes an operation corresponding to the operation information acquired by the operation information acquisition unit.

Abstract: A data processing apparatus and a method, a recognition apparatus, a learning data storage apparatus, a machine learning apparatus, and a program capable of improving recognition accuracy for data of a rare case are provided. A data processing apparatus according to one aspect of the present invention includes a recognition unit that learns using a learning data set, a recognition result correction unit that corrects a recognition result of the recognition unit for data acquired through a data acquisition unit in accordance with an instruction from a user, and a machine learning unit that performs learning of the recognition unit using the data in which the recognition result is corrected. The machine learning unit performs learning of the recognition unit by setting a degree of contribution to learning from the data in which the recognition result is corrected to be higher than a degree of contribution to learning of the recognition unit from learning data included in the learning data set.

Abstract: Provided are a medical image processing apparatus, a medical image processing method, a program, and a diagnosis support apparatus that report a region of interest without hindering observation of the boundary between the region of interest and a region of non-interest in a medical image. A medical image processing apparatus includes a superimposition processing unit that superimposes, on a medical image, a figure for reporting a region of interest included in the medical image. The superimposition processing unit superimposes the figure on an inside of the region of interest such that at least part of a boundary between the region of interest and a region of non-interest is not superimposed with the figure, thereby reporting the region of interest without hindering observation of the boundary between the region of interest and the region of non-interest.

Abstract: The present invention is directed to providing an image processing apparatus and an image processing method that assist an observer of a time-series image in performing smooth observation while attracting attention of the observer.