Abstract: A magnetic resonance imaging apparatus according to an embodiment includes a processor and a memory. The memory stores processor-executable instructions that cause the processor to detect cross-sectional positions of a plurality of cross-sectional images to be acquired in an imaging scan from volume data; acquire the cross-sectional images in sequence based on the cross-sectional positions by executing the imaging scan; and after the first cross-sectional image is acquired in the imaging scan, generate a display image, and display the display image on a display, the display image being an image in which a cross-sectional position of a second cross-sectional image which is detected from the volume data is superimposed on the first cross-sectional image, the second cross-sectional image being a cross-sectional image before being acquired and intersecting with the first cross-sectional image.

Abstract: A treatment system of embodiments includes an imaging system including one or more radiation sources and a plurality of detectors, a first acquirer, a second acquirer, a first deriver, a second deriver, and a calibrator. The radiation sources radiate radiation to an object in a plurality of different directions. The plurality of detectors detect the radiation at different positions. The first acquirer acquires images based on the radiation. The second acquirer acquires position information of a first imaging device in a three-dimensional space. The first deriver derives the position of the object in the images. The second deriver derives the position of a second imaging device in the three-dimensional space based on the position of the object in the images, the position of the first imaging device, and the like. The calibrator performs calibration of the imaging system based on a derivation result of the second deriver.

Abstract: There is provided a check valve capable of effectively suppressing the generation of chattering. The check valve includes: a housing including an inlet channel, a valve chest, and an outlet channel; a seat member provided around the inlet channel, the seat member including a seat portion; a valve element pressed against the seat portion to close the inlet channel; a biasing member configured to push the valve element toward a valve seat; a guide portion provided at the housing and configured to guide the valve element when the valve element moves in an axial direction; and a damper chamber communicating with the valve chest through a space between the valve element and the guide portion, the damper chamber being configured to attenuate axial moving force of the valve element. The biasing member is arranged in the valve chest.

Abstract: A providing apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: store, in the memory, a first machine learning model capable of changing an amount of calculation of a model of a neural network; acquire device information; set, based on the device information, extraction conditions representing conditions for extracting second machine learning models from the first machine learning model; extract the second machine learning models from the first machine learning model based on the extraction conditions; and provide the second machine learning models to a device specified by the device information.

Abstract: According to an embodiment, a learning device includes one or more hardware processors configured to function as a structure search unit. The structure search unit searches for a first learned model structure. The first learned model structure is obtained by selecting search space information in accordance with a target constraint condition of target hardware for each of a plurality of convolution processing blocks included in a base model structure in a neural network model.

Abstract: A robot system according to an embodiment includes one or more processors. The processors acquire first input data predetermined as data affecting an operation of a robot. The processors calculate a calculation cost of inference processing using a machine learning model for inferring control data used for controlling the robot, on the basis of the first input data. The processors infer the control data by the machine learning model set according to the calculation cost. The processors control the robot using the inferred control data.

Abstract: A defect inspection device includes at least one memory storing instructions and at least one processor.

Abstract: A medical image processing device includes a first image acquirer and a tracker. The first image acquirer is configured to acquire a fluoroscopic image of a patient as a first image. The tracker is configured to track an object photographed in the first image on the basis of a first feature common to object images that are a plurality of images of the object obtained by observing the object placed within a body of the patient in a plurality of directions.

Abstract: A learning device according to an embodiment includes one or more hardware processors configured to function as a generation unit, an inference unit, and a training unit. The generation unit generates input data with which an error between a value output from each of one or more target nodes and a preset aimed value is equal to or less than a preset value, the target nodes being in a target layer of a plurality of layers included in a first neural network. The inference unit causes the input data to propagate in a forward direction of the first neural network to generate output data. The training unit trains a second neural network differing from the first neural network by using training data including a set of the input data and the output data.

Abstract: A check valve capable of being reduced in size and suppressing the generation of chattering, including: a seat member arranged at a channel of a fluid including a large-diameter portion, small-diameter portion, and seat portion, the large and small-diameter portions provided at an inner periphery of the seat member, the seat portion provided between the large and small-diameter portions; a valve element pressed against the seat portion to close the channel; and a casing including a valve chest accommodating the valve element so the valve element is movable in an axial direction. The large-diameter portion has an inner diameter larger than an outer diameter of the valve element which enters in the axial direction. A space through which fluid flows is formed between the large-diameter portion and the valve element. The diameter of the large-diameter portion is equal to or less than 1.2 times the diameter of the valve element.

Abstract: An inference apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: acquire at least one control parameter of second machine learning model, the second machine learning model having a size smaller than a size of a first machine learning model input to the inference apparatus; change the first machine learning model to the second machine learning model based on the at least one control parameter; and perform inference in response to input data by using the second machine learning model.

Abstract: According to one embodiment, a contour extraction method for extracting a contour of a target object from an image obtained using an electron beam includes: extracting the contour of the target object from a backscattered electron image; creating a dictionary for associating a secondary electron image obtained from a portion common to the backscattered electron image with the contour; calculating a likelihood of the contour of the target object in a plurality of positions of a newly obtained secondary electron image by referencing the dictionary regarding the newly obtained secondary electron image; and setting a route along which a total sum of the likelihood is maximized out of the plurality of positions as the contour of the target object.

Abstract: A medical image processing apparatus comprising: a first input interface configured to acquire a three-dimensional volume image of an object which is provided with at least one marker, the three-dimensional volume image being generated by imaging the object using a medical examination apparatus; a second input interface configured to acquire geometry information of an imaging apparatus which is used for imaging the object to generate a fluoroscopic image of the object; and a specific-setting-information generator configured to generate specific setting information based on the three-dimensional volume image and the geometry information, the specific setting information being used for setting of imaging for generating an image depicting the at least one marker or setting of image processing of the image depicting the at least one marker.

Abstract: According to one embodiment, a contour extraction method for extracting a contour of a target object from an image obtained using an electron beam includes: extracting the contour of the target object from a backscattered electron image; creating a dictionary for associating a secondary electron image obtained from a portion common to the backscattered electron image with the contour; calculating a likelihood of the contour of the target object in a plurality of positions of a newly obtained secondary electron image by referencing the dictionary regarding the newly obtained secondary electron image; and setting a route along which a total sum of the likelihood is maximized out of the plurality of positions as the contour of the target object.

Abstract: A medical image processing apparatus comprising: a first input interface configured to acquire a three-dimensional volume image of an object which is provided with at least one marker, the three-dimensional volume image being generated by imaging the object using a medical examination apparatus; a second input interface configured to acquire geometry information of an imaging apparatus which is used for imaging the object to generate a fluoroscopic image of the object; and a specific-setting-information generator configured to generate specific setting information based on the three-dimensional volume image and the geometry information, the specific setting information being used for setting of imaging for generating an image depicting the at least one marker or setting of image processing of the image depicting the at least one marker.

Abstract: A medical diagnostic apparatus according to one embodiment comprises processing circuitry. The processing circuitry is configured to acquire a three-dimensional shape of a first part based on a contour of the first part in each of a plurality of sectional images intersecting each other along an extending direction of the first part connecting to a cardiac chamber; acquire a three-dimensional shape of a second part, based on a contour of the second part in a sectional image along an extending direction of the second part connecting to the cardiac chamber; and generate a three-dimensional image representing at least some of the first part, the second part, and the cardiac chamber using a three-dimensional shape representing the cardiac chamber and the three-dimensional shapes of the first part and the second part acquired.

Abstract: According to an embodiment, a learning method of optimizing a neural network, includes updating and specifying. In the updating, each of a plurality of weight coefficients included in the neural network is updated so that an objective function obtained by adding a basic loss function and an L2 regularization term multiplied by a regularization strength is minimized. In the specifying, an inactive node and an inactive channel are specified among a plurality of nodes and a plurality of channels included in the neural network.

Abstract: A medical image processing apparatus comprising: a first input interface configured to acquire a three-dimensional volume image of an object which is provided with at least one marker, the three-dimensional volume image being generated by imaging the object using a medical examination apparatus; a second input interface configured to acquire geometry information of an imaging apparatus which is used for imaging the object to generate a fluoroscopic image of the object; and a specific-setting-information generator configured to generate specific setting information based on the three-dimensional volume image and the geometry information, the specific setting information being used for setting of imaging for generating an image depicting the at least one marker or setting of image processing of the image depicting the at least one marker.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes a processor and a memory. The memory stores processor-executable instructions that cause the processor to detect cross-sectional positions of a plurality of cross-sectional images to be acquired in an imaging scan from volume data; acquire the cross-sectional images in sequence based on the cross-sectional positions by executing the imaging scan; and after the first cross-sectional image is acquired in the imaging scan, generate a display image, and display the display image on a display, the display image being an image in which a cross-sectional position of a second cross-sectional image which is detected from the volume data is superimposed on the first cross-sectional image, the second cross-sectional image being a cross-sectional image before being acquired and intersecting with the first cross-sectional image.

Abstract: A medical-image processing apparatus according to an embodiment includes processing circuitry. The processing circuit acquires an initial value of an outline corresponding vector that corresponds to an outline of a subject included in medical image data. The processing circuitry updates the outline corresponding vector based on a derivative that is acquired by differentiating a cost function with respect to the outline corresponding vector by the outline corresponding vector, and on the initial value of the outline corresponding vector.