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公开(公告)号：US20220198734A1
公开(公告)日：2022-06-23
申请号：US17692172
申请日：2022-03-11
Applicant: FUJIFILM Corporation
Inventor： Akira KUDO , Yoshiro KITAMURA
Abstract: An image generation device derives, for a subject including a specific structure, a subject model representing the subject by deriving each feature amount of the target image having the at least one representation format and combining the feature amounts based on the target image. A latent variable derivation unit derives a latent variable obtained by dimensionally compressing a feature of the subject model according to the target information based on the target information and the subject model. A virtual image derivation unit outputs a virtual image having the representation format represented by the target information based on the target information, the subject model, and the latent variable.
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2.发明公开公开(公告)号：US20240005498A1
公开(公告)日：2024-01-04
申请号：US18357991
申请日：2023-07-24
Applicant: FUJIFILM Corporation
Inventor： Akira KUDO
CPC classification number: G06T7/0012 , G16H30/40 , G06V10/82 , G06T2207/20048 , G06T2207/10072 , G06T2207/20084 , G06T2207/20081 , G06V2201/031
Abstract: By using a learning model having a structure of a generative adversarial network including a first generator configured using a first convolutional neural network that receives an input of a medical image of a first domain and that outputs a first generated image of a second domain, and a first discriminator configured using a second convolutional neural network that receives an input of data including first image data, which is the first generated image or a medical image of the second domain included in a training dataset and coordinate information of a human body coordinate system corresponding to each position of a plurality of unit elements configuring the first image data, and that discriminates authenticity of the input image, a computer acquires a plurality of pieces of training data including the medical image of the first domain and the medical image of the second domain; and performs training processing.
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Patent Agency Ranking
公开(公告)号：US20220139062A1
公开(公告)日：2022-05-05
申请号：US17578465
申请日：2022-01-19
Applicant: FUJIFILM Corporation
Inventor： Akira KUDO , Yoshiro KITAMURA
Abstract: An extraction model is constituted of an encoder that extracts a feature amount of a first image of a first representation format to derive a feature map of the first image, a first decoder that derives a second virtual image of a second representation format different from the representation format of the first image on the basis of the feature map, a first discriminator that discriminates a representation format of an input image and whether the input image is a real image or a virtual image, and outputs a first discrimination result, a second decoder that extracts a region of interest of the first image on the basis of the feature map, and a second discriminator that discriminates whether an extraction result of the region of interest by the second decoder is an extraction result of a first image with ground-truth mask or an extraction result of a first image without ground-truth mask, and outputs a second discrimination result.
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公开(公告)号：US20210374911A1
公开(公告)日：2021-12-02
申请号：US17400142
申请日：2021-08-12
Applicant: FUJIFILM Corporation
Inventor： Akira KUDO , Yoshiro KITAMURA
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.
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5.发明公开公开(公告)号：US20230368442A1
公开(公告)日：2023-11-16
申请号：US18357986
申请日：2023-07-24
Applicant: FUJIFILM Corporation
Inventor： Akira KUDO
CPC classification number: G06T11/008 , G06T7/11 , G06T3/4053 , G06T2200/04 , G06T2207/20084 , G06T2207/20132 , G06T2207/20081 , G06T2207/30004 , G06T2207/10088 , G06T2207/10081
Abstract: A method of generating a trained model uses a first generator configured using a three-dimensional convolutional neural network that receives an input of a three-dimensional image of a first domain and that outputs a three-dimensional generated image of a second domain different from the first domain, and a first discriminator configured using a two-dimensional convolutional neural network that receives an input of a two-dimensional image indicating a cross section image in a first slice plane direction cut out from the three-dimensional generated image of the second domain and that discriminates authenticity of the input two-dimensional image. The method includes performing, by a computer, training the first generator and the first discriminator in an adversarial manner based on training data including a three-dimensional image captured under a first imaging condition and a three-dimensional image captured under a second imaging condition different from the first imaging condition.
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公开(公告)号：US20230214664A1
公开(公告)日：2023-07-06
申请号：US18183954
申请日：2023-03-15
Applicant: FUJIFILM Corporation
Inventor： Akira KUDO
Abstract: A processor inputs a first training image having a first feature to a generator, which is a generative model and generates a training virtual image having a second feature. The processor derives a plurality of types of conversion training images with different observation conditions by performing a plurality of types of observation condition conversion processing on a second training image. The processor derives a plurality of types of conversion training virtual images with the different observation conditions by performing the plurality of types of observation condition conversion processing on the training virtual image. The processor trains the generative model using evaluation results regarding the plurality of types of conversion training images and the plurality of types of conversion training virtual images.
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公开(公告)号：US20210374483A1
公开(公告)日：2021-12-02
申请号：US17400150
申请日：2021-08-12
Applicant: FUJIFILM Corporation
Inventor： Akira KUDO , Yoshiro KITAMURA
Abstract: Provided are a learning method, a learning device, a generative model, and a program that generate an image including high resolution information without adjusting a parameter and largely correcting a network architecture even in a case in which there is a variation of the parts of an image to be input. Only a first image is input to a generator of a generative adversarial network that generates a virtual second image having a relatively high resolution by using the first image having a relatively low resolution, and a second image for learning or the virtual second image and part information of the second image for learning or the virtual second image are input to a discriminator that identifies the second image for learning and the virtual second image.
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