Fujitsu Corporation

Sumitomo Bakelite Company Limited

Daiichi Sankyo Company, Limited

Tokyo Kikai Seisakusho, Ltd.

Takayuki Baba

Susumu Endo

Yusuke Uehara

Daiki Masumoto

Shuichi Shiitani

Masaki Ishihara

Shigemi Nagata

Masahiko Sugimura

Hiroaki Takebe

Rujie Liu

Yasutaka Moriwaki

Hirohisa Naito

Takeshi Hosomi

Akira Miyazaki

Shugo Nakamura

Jun Watanabe

Kenji Murata

Atsushi Oguchi

Kenji Yoshikawa

Noriyasu Haginoya

Yuji Matsuda

Yoshiko Kagoshima

Nobuhiro Miyazaki

Masashi Numata

Masashi Urushihara

Taeko Shinozaki

Maroshi Yuasa

Ryutaro Kanada

Hiroshi Hayai

Tomoaki Hamada

Eri Tokumaru

Mayumi Kitagawa

Shuchi Shiitani

Akiko Kurimoto

Kazuya Hamaya

Machiko Shiroishi

Takashi Abe

Koji Matsumoto

Hiroyuki Okada

Fei Fei Li

Kazumasa Aoki

Makoto Mori

Toshio Kaneko

Hao Yu

Hiroshi Yamakawa

Atsushi Aoyagi

Masaharu Inui

Sokichi Fujita

Tatsuma Muramatsu

Kunihiko Fujii

Takeshi Shida

Yukari Onishi

Masahiko Shimada

Takashi Ohmi

Yuehong Wang

Dai Miyawaki

Masako Yamashita

Kentaro Yabuki

The present invention provides a compound or a pharmaceutically acceptable salt thereof having an inhibitory action on the interaction between menin and an MLL protein. The compound represented by the formula (1) or a pharmaceutically acceptable salt thereof. wherein, in the formula (1), the dotted circle, R, R, R, R, R, R, R, R, Ring Q, W, m and n are each as defined in the description.

Cycloalkane-1,3-diamine derivative

A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes identifying first filter processing applied to a first image that is training data used for machine learning; generating the first image from which characteristics of the identified first filter processing are removed; and generating, by applying second filter processing to the first image from which the characteristics are removed, a second image to be assigned a label identical to a label of the first image, the second image being used in the machine learning as the training data.

Computer-readable recording medium, training data generation method, and training data generation apparatus

A non-transitory computer-readable recording medium having stored an image processing program that causes a computer to execute a process, the process includes extracting a plurality of consecutive pixels corresponding to a first part or a second part of a body, from a pixel column in a predetermined direction of an image of the body, obtaining a statistical value of pixel values of the plurality of consecutive pixels, and identifying a part corresponding to the plurality of consecutive pixels, among the first part or the second part, based on the statistical value.

Image processing apparatus and image processing method

A similar case image search method performed by a computer, the method includes: extracting a lung field area from a medical image and identifying a contour of the lung field area including a chest wall and a mediastinum; identifying a position at which the chest wall and the mediastinum are internally divided and dividing the lung field area into a central area and a peripheral area based on a shape of the lung field area; counting the number of pixels indicating lesions in each of the divided central area and peripheral area; and identifying a similar case image corresponding to similarity level of the number of pixels indicating lesions by referring to a storage unit that stores the number of pixels indicating lesions in each of the areas.

Similar case image search program, similar case image search apparatus, and similar case image search method

An apparatus for processing an image includes a memory and a processor coupled to the memory. The processor is configured to determine a base hue value based on a state of distribution of a first hue value of each pixel of at least one image. The processor is configured to convert the first hue value of each pixel of the at least one image into a second hue value based on the base hue value. The processor is configured to calculate a difference in the second hue value between pixels included in each of a plurality of pixel pairs set in at least one feature region in the at least one image. The processor is configured to calculate a local feature amount of each of the at least one feature region based on the difference in the second hue value.

Apparatus for processing image and method thereof

A non-transitory recording medium stores a program causing a computer to execute a process including determining a reference value on a decision surface classifying a group of image data represented by a plurality of types of feature quantities into two classes in a feature quantity space, based on a feature quantity of given image data that is closest to the decision surface and is classified into one of the classes of the group of image data in the feature quantity space, plotting and displaying pieces of image data of the group of image data along at least one axis indicating a degree of separation of each of the pieces of image data relative to the reference value, modifying, upon receiving a single piece of image data identified from the plotted and displayed pieces of image data, the decision surface based on the identified single piece of image data.

Recording medium, case data generation support system, and case data generation support method

An image retrieval apparatus that retrieves a candidate medical image in diagnosis of diffuse lung disease based on a position of an abnormal shadow in an organ region in a target medical image, the apparatus includes a memory, and a processor coupled to the memory and configured to map the organ region in the target medical image to an image having a predetermined shape to make it identifiable whether the abnormal shadow is distributed over a first portion in the organ region or a second portion in the organ region, occurrence portions of the abnormal shadow within the first portion and the second portion are organizationally different, and calculate a position of the abnormal shadow after the mapping in the image having the predetermined shape.

Image retrieval apparatus and image retrieval method

A non-transitory computer-readable recording medium storing an image interpretation support program that causes a computer to execute a process, the process including generating first data indicating a first internal structure of a predetermined object, based on a first cross-sectional image group acquired with respect to the predetermined object; detecting a structural change of the first internal structure from a second internal structure of the predetermined object, based on second data indicating the second internal structure and the generated first data, the second data being generated based on a second cross-sectional image group acquired at a past time; identifying a new cross-section with respect to the predetermined object based on the structural change; generating a cross-sectional image of the predetermined object with respect to the new cross-section, based on the first cross-sectional image group; and displaying the generated cross-sectional image together with first information indicating the detected structural change.

Cross-sectional image generating apparatus, cross-sectional image generating method, and recording medium

There is provided a distance measuring apparatus including a storing unit configured to store a first image and a second image to be compared with one another; and a calculating unit configured to extract a plurality of feature points from each of the first image and the second image, extract a combination of feature points with high stability of feature values amongst the plurality of feature points in the first image and a combination of feature points with high stability of feature values amongst the plurality of feature points in the second image, and output, as bit strings, the feature values associated with the combination extracted from the first image and the feature values associated with the combination extracted from the second image.

Method and apparatus for distance measurement

An image similarity determination apparatus includes a memory and a processor configured to acquire a first image and a second image, perform selection of a first group and a second group from a plurality of feature points included in the first image and perform selection of a third group and a fourth group from a plurality of feature points included in the second image, calculate feature quantity for each feature point included in the first group and the third group on the basis of luminance and calculate feature quantity for each feature point included in the second group and the fourth group on the basis of hue, and determine similarity between the first image and the second image on the basis of both first comparison of the first group with the third group and second comparison of the second group with the fourth group.

Image similarity determination apparatus and image similarity determination method

A computation unit calculates luminance differences of individual pixel pairs in a feature area and calculates, based thereon, a local feature value formed from bit values respectively corresponding to the pixel pairs. Specifically, the computation unit calculates a specific luminance difference for a specific pixel pair corresponding to a specific bit value and then compares the result with a specified range including a zero point of luminance difference. Then a first value is assigned to the specific bit value when the specific luminance difference is greater than the upper bound of the specified range. A second value is assigned to the same when the specific luminance difference is smaller than the lower bound of the specified range. A predetermined one of the first and second values is assigned to the same when the specific luminance difference falls in the specified range.

Computer-readable storage medium storing image processing program and image processing apparatus

This invention discloses an image retrieval apparatus. The image retrieval apparatus comprises an unlabelled image selector for selecting one or more unlabelled image(s) from an image database; and a main learner for training in each feedback round of the image retrieval, estimating relevance of images in the image database and a user's intention, and determining retrieval results, wherein the main learner makes use of the unlabelled image(s) selected by the unlabelled image selector in the estimation. In addition, the image retrieval apparatus may also include an active selector for selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled image(s) from the image database for the user to label.

Image retrieval apparatus

A non-transitory computer readable storage medium that stores a program for image processing that causes a computer to execute a process including: detecting a change in a lung based on a plurality of images of the chest scanned at different times, the change in the lung indicating that each position of each part of the lung changes toward a specified position of the lung, and outputting the change of the lung.

Computer readable storage medium, apparatus, and method for image processing

An image processor includes: a memory; and a processor, wherein the processor is configured to: extract an arc area in an image captured by an image sensor; and determine whether a portion that protrudes from an arc in the arc area satisfies a first reference regarding a shape and detect the portion as a colony candidate when it is determined that the portion satisfies the first reference.

Image apparatus, image processing method, and computer readable, non-transitory medium

Growing community of inventors

Kawasaki, Japan

Takayuki Baba