Toshiba Medical Systems Corporation

Canon Medical Systems Corporation

Kabushiki Kaisha Toshiba

Shuhei Nitta

Tomoyuki Takeguchi

Taichiro Shiodera

Nobuyuki Matsumoto

Toshiyuki Ono

Shigehide Kuhara

Yukinobu Sakata

Takashi Ida

Hiroaki Nakai

Kensuke Shinoda

Akiyuki Tanizawa

Hidenori Takeshima

Atsushi Yaguchi

Hiroshi Takai

Nobuyasu Ichinose

Naho Imamura

Takuya Fujimaki

Yurika Ogawa

Syuhei Takemoto

Toshimitsu Kaneko

Yoshimori Kassai

Masahiro Sekine

Wataru Asano

Kenichi Shimoyama

Kenzo Isogawa

Masahide Nishiura

Kenta Moriyasu

Satoshi Kawata

Takahiro Ohmure

Takamasa Sugiura

Mai Murashima

Takashi Watanabe

Yasuko Fujisawa

Tomoki Watanabe

Satoshi Sugiura

Masao Yui

Tomoya Okazaki

Yasutoyo Takeyama

Tomoya Tsuruyama

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.

Learning apparatus, method, and non-transitory computer readable medium

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.

Magnetic resonance imaging apparatus, image processing apparatus, and image processing method

A learning device includes a structure search unit that searches for a first learned model structure 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; a parameter search unit that searches for a learning parameter of the neural network model in accordance with the target constraint condition; and a pruning unit that deletes a unit of at least one of the plurality of convolution processing blocks in the first learned model structure based on the target constraint condition and generates a second learned model structure.

Learning device, learning system, and learning method

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.

Robot system and driving method

A learning device includes one or more processors. The processors acquire input data and a target label indicating a correct answer of inference based on the input data. The processors add noise to at least one of the input data and intermediate layer data of the neural network and perform inference by the neural network with respect to the input data. The noise is based on contributions of a plurality of elements included in the input data with respect to an inference result when the input data is input to a neural network. The processors update parameters of the neural network so that the inference result by the neural network matches the target label.

Learning device, learning method, computer program product, and recognition device

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.

Learning device, learning method, and computer program product

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.

Providing apparatus, providing method, and computer program product

A photon counting X-ray CT apparatus according to an embodiment includes: data acquiring circuitry, and processing circuitry. The data acquiring circuitry is configured to allocate energy measured by signals output from a photon counting detector in response to incidence of X-ray photons to any of a plurality of first energy bins so as to acquire a first data group as count data of each of the first energy bins. The processing circuitry is configured to determine a plurality of second energy bins obtained by grouping the first energy bins in accordance with a decomposition target material that is a material to be decomposed in a imaging region, allocate the first data group to any of the second energy bins so as to generate a second data group, and use the second data group to generate an image representing a distribution of the decomposition target material.

Photon counting X-ray CT apparatus

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.

Data generation device, data generation method, and computer program product

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.

Magnetic resonance imaging apparatus and magnetic resonance imaging method

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.

Magnetic resonance imaging apparatus and magnetic resonance imaging method for correcting distortion of a collected magnetic resonance image

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.

Magnetic resonance imaging apparatus

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.

Magnetic resonance imaging apparatus and multi-slice imaging method

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.

Magnetic resonance imaging apparatus and medical image processing method

Growing community of inventors

Tokyo, Japan

Shuhei Nitta