Sony Corporation

Nec Corporation

Mitsubishi Denki Kabushiki Kaisha

Mitsubishi Electric Corporation

Wisconsin Alumni Research Foundation

Ihi Corporation

Sony Group Corporation

Renesas Electronics Corporation

Kabushiki Kaisha Toshiba

Mitsubishi Jidosha Kogyo Kabushiki Kaisha

Fuji Photo Film Company, Limited

Kao Corporation

Nec Electronics Corporation

Canon Kabushiki Kaisha

Japan Science and Technology Agency

Shinji Watanabe

Yoshihiro Kawaoka

Kazuki Aisaka

Junya Sato

Nobuhiro Mikami

Atsumasa Sawada

Kenji Yamane

Takao Yamazaki

Toshihiro Iwasaki

Toru Tanaka

Tatsumi Sakaguchi

Hirokazu Honda

Nobuhiro Ogawa

Hiroaki Matsubara

Tomoshige Chikai

Masato Tamura

Kiminori Ishido

Michiaki Tamakawa

Masahiro Yamada

Tatsuhiko Takahashi

Nobuhiro Hayashi

Kenjiro Fujita

Tokiko Watanabe

Shingo Nakamura

Shotaro Sakumoto

Kaoru Kondo

Takeshi Miyakoshi

Sumikazu Hosoyamada

Yoshikazu Kumagaya

Masahiro Takahashi

Takashi Nakamura

Katsumi Abe

Koji Miyata

Yutaka Hasegawa

Kentaro Mori

Katsuhiko Suzuki

Yusuke Yamada

Kunihiko Hayashi

Yoshihiro Ono

Masahito Yamane

Tsuneo Hayashi

Takeshi Uemori

Takeshi Kunihiro

Nozomu Kamioka

Tsuyoshi Kida

Koji Nishimoto

Kensuke Hayashi

Kenji Sakata

Shota Shimoda

Toru Mitome

Nozomu Nishimura

Rei Murata

Kenichiro Fujii

Tadashi Tubakiji

Akihiro Tanaka

Akira Matsumoto

Nobuyuki Sato

Hiroshi Sakai

Tetsuya Suzuki

Minoru Yoshikawa

Yasuo Suzuki

Satoshi Wachi

Ryoji Nishiyama

Tetsuya Morita

Makoto Igarashi

Fumiaki Nakao

Yoichi Mizutani

Nobuaki Izumi

Hideaki Katashiba

Hiroshi Kyusojin

Masaki Chiba

Takashi Amano

Hitoshi Inoue

Yoichi Hayashi

Akira Furuta

Ichiro Hatakeyama

Tomohiro Hayakawa

Kazuma Takahashi

Nobuaki Yokoyama

Mikio Kitaoka

Toru Taura

Satoshi Matsui

Tomoo Murakami

Yasuko Hatta

Yukio Yamaguchi

Kazunao Oniki

Tomiaki Ishihara

Hiroyasu Suzuki

Terutoshi Uchida

Toshihiko Yamada

Junichiro Enoki

Shuzo Watanabe

Tsuneo Tanabe

Motoji Suzuki

Tsutomu Ooishi

Mitsuyasu Kunihiro

Akira Uetsuki

Shigeru Moribayashi

An estimation result can be more effectively utilized. An information processing apparatus includes a deriving unit () that derives an estimation result of diagnosis for a second pathology image using a trained model on which learning has performed using training data including a plurality of first pathology images, and an identifying unit () that identifies a basis image that serves as a basis for derivation of the estimation result by the trained model from the plurality of first pathology images.

Pathology image diagnosis support apparatus, pathology image diagnosis support method, and pathology image diagnosis support system

The generation deviceaccording to the present application includes an acquisition unitand a generation unit. The acquisition unitacquires a first pathological image captured and an annotation that is information added to the first pathological image and is meta information related to the first pathological image. The generation unitgenerates learning data for evaluating pathological-related information based on a second pathological image according to the second pathological image different from the first pathological image, the learning data being learning data obtained by transcribing the annotation in a manner corresponding to the second pathological image.

Generation device and generation method

An information processing device is provided that includes an identification data acquisition unit that acquires image data for identification in which a first biological region is captured and first site information of the first biological region, and an identification unit that selects an identifier corresponding to the first site information from identifiers associated respectively with a plurality of pieces of site information, and performs an identification process on the basis of the identifier and the image data for identification.

Information processing device, information processing method, and information processing system

A diagnosis support program causes a computer to execute a derivation procedure of deriving, on the basis of a first pathological image corresponding to a first affected tissue, history information regarding a history of viewing the first pathological image, and diagnostic information for the first affected tissue corresponding to the first pathological image, diagnosis support information for supporting diagnosis of a second pathological image corresponding to a second affected tissue to be diagnosed.

Diagnosis support program, diagnosis support system, and diagnosis support method

A learning device, for constructing a learning model for generating a prior acceleration command value for controlling a control target in advance of the time a load on a boiler used for thermal power generation changes, includes: a learner configured to generate the learning model by mechanically learning, as learning data, a data set including a power generation command value for the thermal power generation and the prior acceleration command value which have been used in a previous operation of the boiler.

Learning device and boiler control system

The invention provides a vaccine comprising an effective amount of an isolated recombinant influenza virus comprising a mutant M gene segment that is mutated so that upon viral replication the mutant M gene expresses a functional M1 protein and a mutant M2 protein with a deletion of the cytoplasmic tail and either lacking a transmembrane domain or having a mutated transmembrane domain.

Vaccines comprising mutant attenuated influenza viruses

A medical system comprising: a control part configured to receive pathological image data that is produced by imaging one observation target object and that includes first and second image data, the second image data relating to the observation target object but being different to the first image data, a display control part that causes a displaying part to display thereon the first image data and the second image data; and an annotation processing part that applies an annotation to each of the first image data and the second image data on a basis of inputting by a user.

Medical system, medical apparatus, and medical method

[Problem] To highly accurately color a target image based on information such as morphological features and shapes in the target image. [Solution] According to the present disclosure, an image coloring apparatus is provided. The image coloring apparatus includes: an acquisition unit configured to acquire a decolored image that is decolored; and a coloring unit configured to, based on morphological information obtained from the decolored image, with reference to learning result information obtained as a result of previously learning correspondence between the morphological information and color information, color the decolored image using a color that corresponds to the morphological information. This configuration enables a target image to be highly accurately colored based on information such as morphological features and shapes in the target image.

Image coloring apparatus, image coloring method, image learning apparatus, image learning method, computer program, and image coloring system

The disclosure provides for an isolated recombinant influenza virus having at least one of: a PA gene segment encoding PA with a residue at position 443 that is not arginine, a PB1 gene segment encoding PB1 with a residue at position 737 that is not lysine, a PB2 gene segment encoding PB2 with a residue at position 25 that is not valine or a residue at position 712 that is not glutamic acid, a NS gene segment encoding a NS1 with a residue at position 167 that is not proline, a HA gene segment encoding a HA with a residue at position 380 that is not threonine, or any combination thereof, and methods of making and using the virus.

Mutations that confer genetic stability to additional genes in influenza viruses

[Problem] To shorten the processing time without performing complicated processing during image reading. [Solution] The present disclosure provides an image processing apparatus that includes: a dividing unit that divides a detection region for detecting a feature value of an image into a plurality of regions; a decimation processing unit that performs a decimation process on a pixel value for each of the regions; and a histogram calculating unit that interpolates a pixel value having undergone the decimation process to calculate a histogram of pixel values of the regions. With this configuration, it is possible to shorten the processing time without performing complicated processing during image reading.

Image processing apparatus, image processing method, and program

An image processing apparatus includes an output value obtaining unit, a first interpolation unit, a second interpolation unit, an image generation unit, and a first edge detection unit. The output value obtaining unit obtains respective output values of a plurality of light-receiving elements from an image sensor including the plurality of light-receiving elements arranged two-dimensionally. The first interpolation unit uses a first interpolation algorithm to interpolate a pixel value. The second interpolation unit uses a second interpolation algorithm to interpolate a pixel value. The image generation unit generates an image based on a pixel value, which is interpolated by the first interpolation unit. The first edge detection unit detects an edge using a first edge detection algorithm based on a pixel value, which is interpolated by the second interpolation unit.

Image processing apparatus and image processing method

Provided is art information processing apparatus including a detection unit configured to detect a failure requiring reimaging relating to an image captured using a digital microscope by evaluating the image, and a generation unit configured to, if the failure was detected by the detection unit, generate setting information for setting an imaging condition for during reimaging.

Information processing apparatus, imaging control method, program, digital microscope system, display control apparatus, display control method, and program including detection of a failure requiring reimaging

Provided is an information processing device including an image analysis unit which divides an entire image in which an entire observation target region is imaged into a plurality of regions, and then judges whether an observation target is present in each region, and an imaging control unit which controls imaging of partial images with a magnification higher than a magnification of the entire image corresponding to the regions based on judgment results of the image analysis unit.

Information processing device, information processing method, program, and microscope system

An information processing device according to the present technology includes a determination unit that determines importance related to a cell-specific event of a cell, using image data obtained from a time-series imaging process targeting the cell The information processing device also includes a control unit that controls a process regarding a setting for a target of acquisition of image data in the time-series imaging, on the basis of a determination result of the importance.

Information processing device, information processing method and information processing system

Growing community of inventors

Tokyo, Japan

Shinji Watanabe