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    Classifying device, classifying program, and method of operating classifying device
    11.
    发明授权
    Classifying device, classifying program, and method of operating classifying device 有权
    分类装置，分类程序和操作分类装置的方法

    公开(公告)号：US09483715B2

    公开(公告)日：2016-11-01

    申请号：US14669518

    申请日：2015-03-26

    Applicant: FUJIFILM Corporation

    Inventor： Yoshiro Kitamura

    IPC: G06K9/34 ,  G06K9/62 ,  G06T7/00 ,  G06T11/20 ,  G06T7/60

    CPC classification number: G06K9/6267 ,  G06T7/11 ,  G06T7/162 ,  G06T7/62 ,  G06T11/206 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/30048 ,  G06T2207/30096 ,  G06T2207/30101 ,  G06T2207/30172

    Abstract: When each pixel forming image data is classified into one of N labels (where N>2) which are ordered from 0 to N−1, a binary graph setting unit sets a binary graph where the i-th layer i is a boundary between the label i−1 and the label i of the N labels, a class to which a label equal to or higher than i is assigned corresponds to a virtual label 0, and a class to which a label equal to or smaller than i−1 is assigned corresponds to a virtual label 1, a layer-by-layer labeling unit performs a graph cut operation on the binary graph of each layer, and then a label determining unit determines which of the N labels each pixel belongs to based on the virtual labels assigned to all the binary graphs.

    Abstract translation: 当每个形成图像数据的像素被分类为从0到N-1排列的N个标签（其中N≥2）中的一个时，二进制图设置单元设置二进制图，其中第i层i是 标签i-1和N标签的标签i，分配给等于或高于i的标签的类对应于虚拟标签0，并且等于或小于i-1的标签的类别是 分配对应于虚拟标签1，逐层标签单元对每层的二进制图执行图形剪切操作，然后标签确定单元基于虚拟标签确定每个像素属于哪个N个标签 分配给所有二进制图。

    Image processing device, method and program
    12.
    发明授权
    Image processing device, method and program 有权
    图像处理装置，方法和程序

    公开(公告)号：US08897576B2

    公开(公告)日：2014-11-25

    申请号：US13626503

    申请日：2012-09-25

    Applicant: FUJIFILM Corporation

    Inventor： Yoshiro Kitamura

    IPC: G06K9/62 ,  G06K9/46 ,  G06K9/32 ,  G06T7/00

    CPC classification number: G06K9/6211 ,  G06K2209/051 ,  G06T7/149 ,  G06T7/162 ,  G06T2207/20072 ,  G06T2207/20164 ,  G06T2207/30101 ,  G06T2207/30172

    Abstract: Candidate points belonging to a predetermined structure are extracted from image data DV. A shape model which represents a known shape of the predetermined structure and is formed by model labels having a predetermined connection relationship is obtained. Corresponding points corresponding to the model labels are selected from the extracted candidate points under the following constraints: (a) each model label is mapped with only one of the candidate points or none of the candidate points; (b) each candidate point is mapped with only one of the model labels or none of the model labels; and (c) when a path between two candidate points which are mapped with each pair of the model labels connected with each other is determined, each candidate point which is mapped with none of the model labels is included in only one of the determined paths or none of the determined paths.

    Abstract translation: 从图像数据DV提取属于预定结构的候选点。 获得表示预定结构的已知形状并由具有预定连接关系的模型标签形成的形状模型。 在以下约束下，从提取的候选点中选择与模型标签相对应的对应点：（a）每个模型标签仅映射候选点中的一个或不存在候选点; （b）每个候选点仅与模型标签中的一个或模型标签中的一个进行映射; 和（c）当确定与彼此连接的每对模型标签映射的两个候选点之间的路径时，仅映射了所有模型标签的每个候选点仅包括在确定的路径中的一个或 没有确定的路径。

    Device, method and computer readable recording medium containing program for separating image components
    13.
    发明授权
    Device, method and computer readable recording medium containing program for separating image components 有权
    包含用于分离图像分量的程序的装置，方法和计算机可读记录介质

    公开(公告)号：US08577110B2

    公开(公告)日：2013-11-05

    申请号：US13721222

    申请日：2012-12-20

    Applicant: FUJIFILM Corporation

    Inventor： Yoshiro Kitamura

    IPC: G06K9/00

    CPC classification number: G06K9/34 ,  A61B6/506 ,  G06T5/50 ,  G06T7/0012 ,  G06T2207/10116 ,  G06T2207/30004

    Abstract: A problem inherent to radiographic images, which may occur when an independent component analysis technique is applied to energy subtraction carried out on radiographic images, is solved to achieve separation of image components to be separated with higher accuracy. As preprocessing before the independent component analysis, a spatial frequency band which contains the components to be separated is extracted, pixels of the radiographic images are classified into more than one subsets for each radiographic image based on a value of a predetermined parameter, and/or nonlinear pixel value conversion is applied to the radiographic images based on a value of the predetermined parameter. Alternatively, nonlinear independent component analysis is carried out according to a model using the predetermined parameter.

    Abstract translation: 解决了当将独立分量分析技术应用于在放射线照相图像上执行的能量减去时可能发生的放射照相图像固有的问题，以实现更高精度地分离要分离的图像分量。 作为在独立分量分析之前的预处理，提取包含要分离的分量的空间频带，基于预定参数的值，将放射线照相图像的像素分类为每个放射线照相图像的多于一个子集，和/或 基于预定参数的值将非线性像素值转换应用于放射照相图像。 或者，根据使用预定参数的模型执行非线性独立分量分析。

    Learning method, learning system, learned model, program, and super resolution image generating device
    14.
    发明授权
    Learning method, learning system, learned model, program, and super resolution image generating device 有权

    公开(公告)号：US12217387B2

    公开(公告)日：2025-02-04

    申请号：US17400142

    申请日：2021-08-12

    Applicant: FUJIFILM Corporation

    Inventor： Akira Kudo ,  Yoshiro Kitamura

    IPC: G06T3/4053 ,  G06N3/045 ,  G06N3/088 ,  G06T3/40 ,  G06T3/4046 ,  G06T5/20 ,  G06T5/70

    Abstract: Provided are a learning method and a learning system of a generative model, a program, a learned model, and a super resolution image generating device that can handle input data of any size and can suppress the amount of calculation at the time of image generation. A learning method according to an embodiment of the present disclosure is a learning method for performing machine learning of a generative model that estimates, from a first image, a second image including higher resolution image information than the first image, the method comprising using a generative adversarial network including a generator which is the generative model and a discriminator which is an identification model that identifies whether provided data is data of a correct image for learning or data derived from an output from the generator and implementing a self-attention mechanism only in a network of the discriminator among the generator and the discriminator.

    Machine learning device and method
    15.
    发明授权
    Machine learning device and method 有权

    公开(公告)号：US12159411B2

    公开(公告)日：2024-12-03

    申请号：US16996871

    申请日：2020-08-18

    Applicant: FUJIFILM Corporation

    Inventor： Deepak Keshwani ,  Yoshiro Kitamura

    IPC: G06N3/08 ,  G06F18/24 ,  G06T7/00 ,  G06T7/11 ,  G06V10/82

    Abstract: Provided is a machine learning device and method that enables machine learning of labeling, in which a plurality of labels are attached to volume data at one effort with excellent accuracy, using training data having label attachment mixed therein.
    A probability calculation unit (14) calculates a value (soft label) indicating a likelihood of labeling of a class Ci for each voxel of a second slice image by means of a learned teacher model (13a). A detection unit (15) detects “bronchus” and “blood vessel” for the voxels of the second slice image using a known method, such as a region expansion method and performs labeling of “bronchus” and “blood vessel”. A correction probability setting unit (16) replaces the soft label with a hard label of “bronchus” or “blood vessel” detected by the detection unit (15). A distillation unit (17) performs distillation of a student model (18a) from the teacher model (13a) using the soft label after correction by means of the correction probability setting unit (16). With this, the learned student model (18a) is obtained.

    Prediction apparatus, prediction method, prediction program
    16.
    发明授权
    Prediction apparatus, prediction method, prediction program 有权

    公开(公告)号：US11348242B2

    公开(公告)日：2022-05-31

    申请号：US16371983

    申请日：2019-04-01

    Applicant: FUJIFILM Corporation

    Inventor： Yoshiro Kitamura

    IPC: G06K9/00 ,  G06T7/00 ,  G06T7/33 ,  G06T7/174 ,  G06N20/00 ,  G06T7/11

    Abstract: A prediction apparatus includes a learning section that performs machine learning in which, with respect to a combination of different types of captured images obtained by imaging the same subject, one captured image is set to an input and another captured image is set to an output to generate a prediction model; and a controller that performs a control for inputting a first image to the prediction model as an input captured image and outputting a predicted second image that is a captured image having a type different from that of the input captured image.

    Image processing apparatus, and operation method and program therefor
    17.
    发明授权
    Image processing apparatus, and operation method and program therefor 有权

    公开(公告)号：US10068148B2

    公开(公告)日：2018-09-04

    申请号：US14642045

    申请日：2015-03-09

    Applicant: FUJIFILM Corporation

    Inventor： Yoshiro Kitamura ,  Tsutomu Inoue

    IPC: G06K9/46 ,  G06K9/52 ,  G06K9/62 ,  G06T7/00 ,  G06T7/10 ,  G06T7/12 ,  G06T7/143 ,  G06T7/149

    Abstract: For assigning a binary label representing belonging to a target region or not to each pixel in an image: a predicted shape of the target region is set; a pixel group including N pixels is selected, where N is a natural number of 4 or more, which have a positional relationship representing the predicted shape; and an energy function is set, which includes an N-th order term in which a variable is a label of each pixel of the pixel group, so that a value of the N-th order term is at a minimum value when a combination of the labels assigned to the pixels of the pixel group is a pattern matching the predicted shape, and increases in stages along with an increase in a number of pixels to which a label different from the pattern is assigned. The labeling is performed by minimizing the energy function.

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