Abstract: There are provided an ultrasound diagnostic apparatus and a control method of the ultrasound diagnostic apparatus which can obtain a cross-sectional image in which a structure of a lesion part is accurately reproduced.

Abstract: An information processing apparatus includes a processor configured to estimate a waveform of heartbeats by inputting a waveform of a measured pulse wave to a model constructed by mounting a pulse wave measurement device and a heartbeat measurement device to a test subject and calculating the relationship between respective waveforms output from the devices.

Abstract: An information processing apparatus includes a memory and a processor configured to make a division of a content of a talk input by a user into talk content elements for each of topics of the talk content elements, and change a display mode of digest information representing the content and to be displayed on a display screen of a third party. The display mode is changed on a basis of a relationship between the topics.

Abstract: A person identification device includes: an extractor that, from an image containing a person, extracts a first characteristic quantity related to a face of the person and a second characteristic quantity related to a body of the person; and an identifying unit that identifies the person based on a third characteristic quantity calculated by assigning a weight to each of the first characteristic quantity and the second characteristic quantity extracted.

Abstract: An information processing apparatus includes a group generation unit that generates search action groups including plural search actions, based on occurrence time of each of search actions occurring along a time series, and a specifying unit that specifies a search action included in an identical search event, based on a group relevance between the search action groups.

Abstract: An image pattern recognition device includes: a data reception unit that receives data; a supervision reception unit that receives supervision; and an artificial neural network processing unit that performs artificial neural network processing. The artificial neural network processing unit includes a first sub-network including one or more layers that process a main task, a second sub-network including one or more layers that process a sub-task, and a third sub-network including one or more layers that do not belong to any of the first sub-network and the second sub-network. The third sub-network includes a branch processing unit that outputs a value same as an input feature amount to a plurality of layers. The first sub-network includes a coupling processing unit that couples inputs from the plurality of layers and outputs a result.

Abstract: An information processing apparatus includes an acquisition unit that acquires, from plural pieces of first information viewed by a user, content information being information of contents described in the first information, an extraction unit that extracts a location in which the user has performed editing, from second information as a target of the user working, and a specifying unit that specifies the first information viewed during a period in which the user performs editing at the location or viewed before and after the editing among the plural pieces of first information from the content information.

Abstract: A data classification device includes an estimation unit that estimates, for each of one or more classes provided for learning data pieces in a feature-amount-data space that includes multiple learning data pieces, probability densities of learning data pieces belonging to the class and learning data pieces not belonging to the class around a judgment target data piece in the feature-amount-data space, a calculation unit that calculates, based on the probability densities, an index indicating how much the judgment target data piece is likely to belong to the class, and a judgment unit that judges which class the judgment target data piece belongs to by using the index. Based on distribution of positive data pieces belonging to the class and negative data pieces not belonging to the class around the judgment target data piece, the estimation unit determines a size of a region used for the estimation.

Abstract: An image-feature-value calculating unit extracts an image feature value of an image of a cell candidate area. An NRBC discriminating unit uses a pre-trained discriminator to identify whether or not a target cell is shown in the cell candidate area, on the basis of the image feature value of the image of the cell candidate area. When the cell candidate area is identified as an area in which a target cell is shown, a discrimination result display unit displays the image of the cell candidate area. When the cell candidate area is identified as an area in which a target cell is shown, a discriminator training unit trains the discriminator by using the image feature value of the image of the cell candidate area as a training sample on the basis of a user input about whether or not a target cell is shown in the cell candidate area.

Abstract: There is provided a class estimation apparatus. A data reception unit receives an input of a feature amount of data. An identification device reception unit receives an input of an identification device having a tree structure. A table storage unit stores a probability table and a feature amount table in a leaf node of the tree structure. A probability table correction unit corrects the probability table. A class estimation unit estimates a class of the data.

Abstract: A person identification device includes: an extractor that, from an image containing a person, extracts a first characteristic quantity related to a face of the person and a second characteristic quantity related to a body of the person; and an identifying unit that identifies the person based on a third characteristic quantity calculated by assigning a weight to each of the first characteristic quantity and the second characteristic quantity extracted.

Abstract: There is provided a non-transitory computer-readable medium storing a program causing a computer to execute a process. The process includes: accepting a search keyword; retrieving, from information items posted by users, a posted information item including the accepted search keyword, each of the posted information items including at least either of a text information item and an image information item, and acquiring posted information items which are within a predetermined chronological range with respect to the posted information item including the search keyword; and classifying, as image information items related to the search keyword, some of image information items included in the posted information items that have been acquired, and performing first determination of, for each of the classified image information items, whether or not a user who posted an information item including the classified image information item took an action related to the search keyword.

Abstract: An image processing apparatus includes a number-of-target-cells estimating unit for estimating, on the basis of a feature of a target sample, the number of target cells included in the target sample, and a detection parameter setting unit for setting, on the basis of the estimated number of target cells, a detection parameter regarding a process of detecting target cells in a captured image of the target sample.

Abstract: An image processing apparatus includes a detection-difficulty determination unit and a detection-parameter setting unit. The detection-difficulty determination unit calculates a degree of detection difficulty indicating a degree of difficulty in detection of a target cell contained in a specimen, based on a test object condition of the specimen. The detection-parameter setting unit sets a detection parameter for the detection of the target cell from a captured image of the specimen, based on the degree of detection difficulty.

Abstract: A detection control device includes a detection target image acquiring unit that acquires an imaged image resulting from imaging a specimen that is to be subjected to the detection of the target cell, a processing target region setting unit that sets a plurality of processing target regions in the imaged image, a processing target region distributing unit that distributes, in accordance with a distribution amount of data to each of a plurality of processing devices that detect the target cell in an image region, the plurality of processing target regions to the plurality of processing devices, and a detection result acquiring unit that acquires, from each of the plurality of processing devices, a result of a process of detecting the target cell in the processing target regions distributed to the respective plurality of processing devices.

Abstract: An image processing device 20 acquires captured images obtained by imaging a sample including a target cell, sets an object region for detecting the target cell in the captured images, extracts a plurality of cell regions each including a cell from a peripheral region including a region other than the object region, sets a range of a feature quantity of the target cell based on a feature quantity obtained from each of the plurality of cell regions, and determines whether or not the target cell is included in the object region when the feature quantity obtained from the object region is included in the set range of the feature quantity.

Abstract: An image processing apparatus includes a reception section, an image extraction section, a forming section, and a comparison section. The reception section receives a video. The image extraction section extracts target object images from multiple frames that constitute the video received by the reception section. The forming section forms multiple target object images among the target object images extracted by the image extraction section into one unit, the multiple target object images being temporally apart from each other. The comparison section makes a comparison on the basis of the unit formed by the forming section.

Abstract: An image processing apparatus includes a unifying unit, a memory, a storing unit, a setting unit, a selecting unit, an extracting, and a determining unit. The unifying unit unifies images of identification target regions cut out from a learning image. The memory stores a learning model. The storing unit stores identification target images converted into images of different image sizes. The setting unit sets a position and a size of a candidate region which is likely to include an identification target object of an identification target image. The selecting unit selects an identification target image of an image size with which the size of the cut-out candidate region is closest to the fixed size. The extracting unit extracts the information. The determining unit determines a target object included in the image of the candidate region.

Abstract: A device comprises: a nucleus-candidate-region extracting section that extracts, from a captured image obtained by image-capturing a sample piece including a target cell having a nucleus, a nucleus candidate region corresponding to the nucleus; a basic-probability-information acquiring section that acquires, for each of a plurality of determination subject regions determined on the basis of the nucleus candidate region extracted by the nucleus-candidate-region extracting section, basic probability information indicating probability that an image in the determination subject region is an image of the target cell, on the basis of a feature amount of the image of the determination subject region; and a probability-information calculating section that calculates probability information indicating probability that an image in a display subject region corresponding to the nucleus candidate region is the image of the target cell, on the basis of the basic probability information acquired for each of the plurality of

Abstract: A non-transitory computer readable medium stores a program that causes a computer to execute a position conversion process. The process includes storing actual layout information and virtual layout information, the actual layout information defining a layout of a first subject within an actual structure, the virtual layout information defining a layout of a second subject within a virtual structure; acquiring a human position within the actual structure; and converting the human position in the actual layout information into the human position in the virtual layout information.