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公开(公告)号：US20240135539A1
公开(公告)日：2024-04-25
申请号：US18544886
申请日：2023-12-19
Applicant: NEC Corporation
Inventor： Kazuhiro WATANABE , Yuji IWADATE , Masahiro SAIKOU , Akinori EBIHARA , Taiki MIYAGAWA
CPC classification number: G06T7/0012 , G06V10/761 , G06T2207/10068 , G06T2207/20084 , G06T2207/30096
Abstract: The image processing device 1X includes an acquisition means 30X and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means 34X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means 34X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.
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公开(公告)号：US20240127442A1
公开(公告)日：2024-04-18
申请号：US18544757
申请日：2023-12-19
Applicant: NEC Corporation
Inventor： Kazuhiro WATANABE , Yuji IWADATE , Masahiro SAIKOU , Akinori EBIHARA , Taiki MIYAGAWA
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06T2207/10068 , G06T2207/20084 , G06T2207/30096
Abstract: The image processing device 1X includes an acquisition means 30X, a variation detection means 311X, a selection means 312X, and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means 311X detects a degree of variation between the endoscopic images. The selection means 312X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means 34X detects the lesion based on a selection model that is either the first model or the second model selected.
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公开(公告)号：US20250095145A1
公开(公告)日：2025-03-20
申请号：US18559635
申请日：2023-01-11
Applicant: NEC CORPORATION
Inventor： Kenichi KAMIJO , Yuji IWADATE , Takuma IGARASHI
Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic image taken by an endoscope. The first lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine. The second lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine. The output means outputs at least one of a detection result of the first lesion detection means and a detection result of the second lesion detection means.
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公开(公告)号：US20240212142A1
公开(公告)日：2024-06-27
申请号：US18409907
申请日：2024-01-11
Applicant: NEC Corporation
Inventor： Yuji IWADATE
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06T2207/10068 , G06T2207/30096
Abstract: The image processing device 1X includes a first acquisition means 30X, a second acquisition means 31X, and an inference means 33X. The first acquisition means 30X acquires a set value of a first index indicating an accuracy relating to a lesion analysis. The second acquisition means 31X acquires, for each of plural models which make inference regarding a lesion, a predicted value of a second index, which is an index of the accuracy other than the first index, on an assumption that the set value of the first index is satisfied. The inference means 33X makes inference regarding the lesion included in an endoscopic image of an examination target, based on the predicted value of the second index and the plural models.
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公开(公告)号：US20250078254A1
公开(公告)日：2025-03-06
申请号：US18288689
申请日：2023-05-19
Applicant: NEC Corporation
Inventor： Yuji IWADATE
IPC: G06T7/00 , G06T3/4084
Abstract: The image processing device 1X includes an acquisition means 31X, a selection means 32X, and a determination means 33X. The acquisition means 31X is configured to acquire data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by a photographing unit provided in an endoscope. The selection means 32X is configured to select partial data that is a part of the data. The determination means 33X is configured to make a determination regarding an attention point to be noticed in the examination target based on the partial data. It can be used for assisting user's decision making,
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Patent Agency Ranking
公开(公告)号：US20240161294A1
公开(公告)日：2024-05-16
申请号：US18544883
申请日：2023-12-19
Applicant: NEC Corporation
Inventor： Kazuhiro WATANABE , Yuji IWADATE , Masahiro SAIKOU , Akinori EBIHARA , Taiki MIYAGAWA
CPC classification number: G06T7/0012 , G06V10/761 , G06T2207/10068 , G06T2207/20084 , G06T2207/30096
Abstract: The image processing device 1X includes an acquisition means 30X, a variation detection means 311X, a selection means 312X, and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means 311X detects a degree of variation between the endoscopic images. The selection means 312X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means 34X detects the lesion based on a selection model that is either the first model or the second model selected.
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公开(公告)号：US20250157028A1
公开(公告)日：2025-05-15
申请号：US18839459
申请日：2022-02-28
Applicant: NEC Corporation
Inventor： Kazuhiro WATANABE , Masahiro SAIKOU , Yuji IWADATE
IPC: G06T7/00 , G06V10/25 , G06V10/764 , G16H50/20
Abstract: The image processing device 1X includes an acquisition means 30X, a score calculation means 31X, and a classification means 32X. The acquisition means 30X acquires a captured image obtained by photographing an examination target by a photographing unit provided in an endoscope. The score calculation means 31X calculates, based on the captured images in time series obtained from a start time, a score regarding a likelihood of a presence of a region of interest in the captured images. Here, upon detecting a variation in the captured images, the score calculation means 31X sets a time after the variation as the start time. The classification means 32X classifies, based on the score, the captured images in time series if a predetermined condition is satisfied.
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公开(公告)号：US20250078259A1
公开(公告)日：2025-03-06
申请号：US18561130
申请日：2023-08-18
Applicant: NEC Corporation
Inventor： Kazuhiro WATANABE , Yuji IWADATE , Masahiro SAIKOU , Akinori EBIHARA , Taiki MIYAGAWA
Abstract: The image processing device 1X includes an acquisition means 30X and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means 34X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means 34X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.
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9.发明公开公开(公告)号：US20240233121A1
公开(公告)日：2024-07-11
申请号：US18538078
申请日：2023-12-13
Applicant: NEC Corporation
Inventor： Kenichi KAMIJO , Yuji IWADATE , Takuma IGARASHI
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06T2207/10016 , G06T2207/10068 , G06T2207/20081 , G06T2207/30092 , G06T2207/30096
Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic image taken by an endoscope. The first lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine. The second lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine. The output means outputs at least one of a detection result of the first lesion detection means and a detection result of the second lesion detection means.
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公开(公告)号：US20250104223A1
公开(公告)日：2025-03-27
申请号：US18574123
申请日：2023-08-31
Applicant: NEC Corporation
Inventor： Yuji IWADATE
Abstract: The image processing device 1X includes a first acquisition means 30X, a second acquisition means 31X, and an inference means 33X. The first acquisition means 30X acquires a set value of a first index indicating an accuracy relating to a lesion analysis. The second acquisition means 31X acquires, for each of plural models which make inference regarding a lesion, a predicted value of a second index, which is an index of the accuracy other than the first index, on an assumption that the set value of the first index is satisfied. The inference means 33X makes inference regarding the lesion included in an endoscopic image of an examination target, based on the predicted value of the second index and the plural models.
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