Abstract: A gap formed in a front portion of a door is easily and reliably filled so that occurrence of noise is reduced. A frame front side portion retaining a glass run front side portion being inserted is provided in a front portion of a window frame. A front side of an outer panel is provided with a mirror base to which a mirror base cover is attached. A rear edge of the mirror base is provided with a rear plate portion extending to a cabin inner side. A gap is formed between the frame front side portion and the rear plate portion. An outer sealing plate portion overlapping with a cabin outer side of the mirror base is provided with a gap filling portion extending downward.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

Abstract: A data generation device includes one or more processors. The processors input input data into a neural network and obtain an inference result of the neural network The processors calculate a first loss and a second loss. The first loss becomes smaller in value as a degree of matching between the inference result and a target label becomes larger. The target label indicates a correct answer of the inference. The second loss is a loss based on a contribution degree to the inference result of a plurality of elements included in the input data and the target label. The processors update the input data based on the first loss and the second loss.

Abstract: According to one embodiment, a learning apparatus includes a setting unit, a training unit, and a display. The setting unit sets one or more second training conditions based on a first training condition relating to a first trained model. The training unit trains one or more neural networks in accordance with the one or more second training conditions and generates one or more second trained models which execute a task identical to a task executed by the first trained model. The display displays a graph showing an inference performance and calculation cost of each of the one or more second trained models.

Abstract: According to one embodiment, a learning apparatus includes processing circuitry. The processing circuitry acquires a plurality of learning samples to be learned and a plurality of target labels associated with the respective learning samples, iteratively learns a learning model so that a learning error between output data corresponding to the learning sample and the target label is small with respect to the learning model to which the output data is output by inputting the learning sample, and displays a layout image in which at least some of the learning samples are arranged based on a learning progress regarding the iterative learning of the learning model and a plurality of the learning errors.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry acquires a first resonance frequency distribution of a first tissue and a second resonance frequency distribution of a second tissue which is different from the first tissue. The processing circuitry calculates a center frequency of a frequency-selective pulse that suppresses or emphasizes either one of the first tissue and the second tissue in accordance with the first and second resonance frequency distributions. The processing circuitry collects a magnetic resonance signal after the frequency-selective pulse is applied at the calculated center frequency.

Abstract: A machine learning model compression system according to an embodiment includes one or more hardware processors configured to: select a layer of a trained machine learning model in order from an output side to an input side of the trained machine learning model; calculate, in units of an input channel, a first evaluation value evaluating a plurality of weights included in the selected layer; sort, in ascending order or descending order, the first evaluation values each calculated in units of the input channel; select a given number of the first evaluation values in ascending order of the first evaluation values; and delete the input channels used for calculation of the selected first evaluation values.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry calculates a static magnetic field correction amount based on a static magnetic field distribution of a first imaging range narrower than a second imaging range. The processing circuitry collects a magnetic resonance (MR) image of the second imaging range under a static magnetic field which is corrected based on the static magnetic field correction amount. The processing circuitry corrects distortion of the collected MR image.

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: An MRI apparatus includes imaging control circuitry that performs shimming imaging for collecting a first MR signal, and multi-slice imaging for collecting a second MR signal along with radiation of a non-region-selective prepulse, and processing circuitry that generates static magnetic field distributions of the slices, determines a first center frequency of an RF pulse corresponding to each slice and a second center frequency of the prepulse based on the static magnetic field distribution, and determines an order of slices for collecting the second MR signal in accordance with the first and/or second center frequencies, wherein the imaging control circuitry performs the multi-slice imaging in accordance with the order and the first and second center frequencies.

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 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 magnetic resonance imaging apparatus includes an interface, processing circuitry, and imaging control circuitry. The interface inputs, to a locator image, a position-indicating region indicating a position in a displayed cross section. The processing circuitry determines a collection direction relating to multi-slice imaging based on a static magnetic field distribution relating to the position-indicating region and the position-indicating region. The imaging control circuitry performs the multi-slice imaging in the collection direction to a plurality of slices in an imaging region which includes at least the position-indicating region.

Abstract: A magnetic resonance imaging apparatus according to embodiments includes processing circuitry. The processing circuitry configured to acquire layout information which defines a layout of cross-sectional images on a localizer screen, to detect cross-sectional positions of the cross-sectional images from MR data, and to generate the localizer screen according to the layout information, the localizer screen including all or a part of the plurality of cross-sectional images generated on the basis of the plurality of cross-sectional positions.

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: A magnetic resonance imaging apparatus includes processing circuitry calculating a 0-order shimming value for correcting 0-order components of inhomogeneity of a static magnetic field of a collection region in a multi-slice collection for each of slices in the collection region, first-order shimming values for correcting first-order components of the inhomogeneity for each of the slices in the collection region, and multiple-order shimming values for correcting second or higher-order components of the inhomogeneity over the entire of the collection region, by using a distribution of the static magnetic field in the collection region, and imaging control circuitry performing the multi-slice collection to the collection region by using the 0-order, first-order, and multiple-order shimming values.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes processing circuitry. The processing circuitry generates a plurality of cross-sectional images for setting a sectional position to be collected in main imaging based on a characteristic portion of a target detected in three-dimensional data. The processing circuitry lists the cross-sectional images on a display and superimposes a mark corresponding to the characteristic portion on at least one of the cross-sectional images. The processing circuitry receives a setting operation to determine the sectional position. The processing circuitry causes, when the mark is selected in the setting operation, a cross-sectional image to be emphasized a sectional position of which is defined using the characteristic portion corresponding to the mark among the listed cross-sectional images. The processing circuitry performs main imaging based on the sectional position after the setting operation.

Abstract: A medical image processing apparatus according to a present embodiment includes processing circuitry. The processing circuitry is configured to accept an operation for a region of interest (ROI) GUI and a guide GUI on a screen on which a medical image is displayed, the ROI GUI being for setting a ROI on the medical image, the guide GUI being for guiding a setting of the ROI on the medical image. The processing circuitry is configured to decide whether to move the ROI GUI and the guide GUI in a manner interlocked with each other or not according to a preset condition, when a turning operation or a sliding operation for any one of the ROI GUI and the guide GUI is accepted.