Kabushiki Kaisha Toshiba

Canon Medical Systems Corporation

Atsushi Yaguchi

Yukinobu Sakata

Akiyuki Tanizawa

Shuhei Nitta

Yasunori Taguchi

Wataru Asano

Tomoya Okazaki

Toshimitsu Kaneko

Shinsuke Tsukagoshi

Tomoki Watanabe

Tatsuya Kimoto

Ryusuke Hirai

Yasutoyo Takeyama

Kyoka Sugiura

Misaki Haratake

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 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 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

According to one embodiment, an image processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data. The processing circuitry is configured to obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern in a predetermined region of a medical image for each of a plurality of textual patterns based on the medical image data. The processing circuitry is configured to calculate feature values in the predetermined region of the medical image based on the spatial distribution obtained for the each of the plurality of textual patterns.

Image processing apparatus, medical image diagnostic apparatus, and program

An image processing apparatus includes processing circuitry configured: to obtain a plurality of images taken so as to include a target site of a subject in temporal phases; and to calculate an index indicating a state of an adhesion at a boundary between a first site of the subject corresponding to a first region and a second site of the subject corresponding to a second region, by using classification information used for classifying each of pixels into one selected from between a first class related to the first region and a second class related to a second region positioned adjacent to the first region in a predetermined direction, on a basis of mobility information among the images in the temporal phases with respect to the pixels in the images that are arranged in the predetermined direction across the boundary between the first region and the second region of the images.

Image processing apparatus and medical image taking apparatus

A medical image processing apparatus includes: a receiver which receives target image data that is including a first perspective image and a second perspective image, photographing an object from different directions; a first acquirer which acquires positional information of a specified point on the first perspective image; a second acquirer which acquires positional information of a candidate point on the second perspective image, the candidate point corresponding to the specified point; and a generator which generates a first enlarged image obtained by enlarging a part of the first perspective image neighboring the specified point and a second enlarged image obtained by enlarging a part of the second perspective image neighboring the candidate point.

Growing community of inventors

Tokyo, Japan

Atsushi Yaguchi