公开(公告)号：US11869190B2

公开(公告)日：2024-01-09

申请号：US17713026

申请日：2022-04-04

申请人： Niigata University ,  Denka Company Limited

发明人： Toshifumi Wakai ,  Shujiro Okuda ,  Yoshifumi Shimada ,  Hiroshi Izutsu ,  Keisuke Kodama

IPC分类号： G06K9/00 ,  G06T7/00 ,  G01N1/30 ,  G01N33/483

CPC分类号： G06T7/0014 ,  G01N1/30 ,  G01N33/4833 ,  G01N2001/302 ,  G06T2207/10024 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/30028 ,  G06T2207/30096

摘要： A method, a program, and a method determining hypermutated type cancer with higher accuracy than before is provided.
Provided is a system for determining hypermutated cancer comprising, an input unit configured to be capable of inputting a plurality of first image data, a plurality of second image data and a plurality of third image data, wherein the first image data represents an image of a pathological section of stained hypermutated cancer, the second image data represents an image of a pathological section of cancer which is not hypermutated, and is stained same as the pathological section of the first image data, and the third image data represents an image of a pathological section of cancer which is newly determined whether hypermutated or not, and is stained same as the pathological section of the first image data, a holding unit configured to be capable of holding a first image data and a second image data, a machine learning execution unit configured to be capable of generating a determination model determining whether a cancer is hypermutated or not, using the first image data and the second image data held by the holding unit as training data, and a determining unit configured to be capable of determining whether the third image data represents an image of hypermutated cancer or not, by inputting the third image data into the determination model.

公开(公告)号：US20220301167A1

公开(公告)日：2022-09-22

申请号：US17713026

申请日：2022-04-04

申请人： Niigata University ,  Denka Company Limited

发明人： Toshifumi Wakai ,  Shujiro Okuda ,  Yoshifumi Shimada ,  Hiroshi Izutsu ,  Keisuke Kodama

IPC分类号： G06T7/00 ,  G01N1/30 ,  G01N33/483

摘要： A method, a program, and a method determining hypermutated type cancer with higher accuracy than before is provided.
Provided is a system for determining hypermutated cancer comprising, an input unit configured to be capable of inputting a plurality of first image data, a plurality of second image data and a plurality of third image data, wherein the first image data represents an image of a pathological section of stained hypermutated cancer, the second image data represents an image of a pathological section of cancer which is not hypermutated, and is stained same as the pathological section of the first image data, and the third image data represents an image of a pathological section of cancer which is newly determined whether hypermutated or not, and is stained same as the pathological section of the first image data, a holding unit configured to be capable of holding a first image data and a second image data, a machine learning execution unit configured to be capable of generating a determination model determining whether a cancer is hypermutated or not, using the first image data and the second image data held by the holding unit as training data, and a determining unit configured to be capable of determining whether the third image data represents an image of hypermutated cancer or not, by inputting the third image data into the determination model.

公开(公告)号：US11295447B2

公开(公告)日：2022-04-05

申请号：US16964550

申请日：2019-02-07

申请人： Niigata University ,  Denka Company Limited

发明人： Toshifumi Wakai ,  Shujiro Okuda ,  Yoshifumi Shimada ,  Hiroshi Izutsu ,  Keisuke Kodama

IPC分类号： G06K9/00 ,  G06T7/00 ,  G01N1/30 ,  G01N33/483

摘要： A method, a program, and a method determining hypermutated type cancer with higher accuracy than before is provided.
Provided is a system for determining hypermutated cancer comprising, an input unit configured to be capable of inputting a plurality of first image data, a plurality of second image data and a plurality of third image data, wherein the first image data represents an image of a pathological section of stained hypermutated cancer, the second image data represents an image of a pathological section of cancer which is not hypermutated, and is stained same as the pathological section of the first image data, and the third image data represents an image of a pathological section of cancer which is newly determined whether hypermutated or not, and is stained same as the pathological section of the first image data, a holding unit configured to be capable of holding a first image data and a second image data, a machine learning execution unit configured to be capable of generating a determination model determining whether a cancer is hypermutated or not, using the first image data and the second image data held by the holding unit as training data, and a determining unit configured to be capable of determining whether the third image data represents an image of hypermutated cancer or not, by inputting the third image data into the determination model.

公开(公告)号：US20210035300A1

公开(公告)日：2021-02-04

申请号：US16964550

申请日：2019-02-07

申请人： Niigata University ,  Denka Company Limited

发明人： Toshifumi Wakai ,  Shujiro Okuda ,  Yoshifumi Shimada ,  Hiroshi Izutsu ,  Keisuke Kodama

IPC分类号： G06T7/00 ,  G01N33/483 ,  G01N1/30

摘要： A method, a program, and a method determining hypermutated type cancer with higher accuracy than before is provided.
Provided is a system for determining hypermutated cancer comprising, an input unit configured to be capable of inputting a plurality of first image data, a plurality of second image data and a plurality of third image data, wherein the first image data represents an image of a pathological section of stained hypermutated cancer, the second image data represents an image of a pathological section of cancer which is not hypermutated, and is stained same as the pathological section of the first image data, and the third image data represents an image of a pathological section of cancer which is newly determined whether hypermutated or not, and is stained same as the pathological section of the first image data, a holding unit configured to be capable of holding a first image data and a second image data, a machine learning execution unit configured to be capable of generating a determination model determining whether a cancer is hypermutated or not, using the first image data and the second image data held by the holding unit as training data, and a determining unit configured to be capable of determining whether the third image data represents an image of hypermutated cancer or not, by inputting the third image data into the determination model.