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公开(公告)号：US20240221373A1
公开(公告)日：2024-07-04
申请号：US18288380
申请日：2022-04-20
Applicant: DEEP BIO INC.
Inventor： Jun Young CHOI , Tae Yeong KWAK , Sun Woo KIM
CPC classification number: G06V10/82 , G06T7/0012 , G16H50/20 , G06T2207/30068 , G06T2207/30096 , G06V2201/032
Abstract: A training method for training an artificial neural network capable of determining a breast cancer lesion area in consideration of both microscopic features and macroscopic features of biological tissue, and a computing system for performing same. A method is provided for training an artificial neural network, comprising steps in which: an artificial neural network training system acquires a slide image of a biological tissue slide; the artificial neural network training system acquires, from the slide image, a first high-resolution patch to an Nth high-resolution patch; the artificial neural network training system acquires an ith low-resolution patch corresponding to an ith high-resolution patch (1
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公开(公告)号：US11995819B2
公开(公告)日：2024-05-28
申请号：US17266098
申请日：2019-08-07
Applicant: DEEP BIO INC.
Inventor： Sang Hun Lee , Sun Woo Kim
CPC classification number: G06T7/0012 , G06F40/143 , G06N3/08 , G06T7/13 , G06T7/136 , G06T7/90 , G16H15/00 , G06T2207/20021 , G06T2207/20084 , G06T2207/30024 , G06T2207/30081 , G06T2207/30096
Abstract: A system and a method that output in both a machine-readable and a human-readable format, a result obtained by performing a diagnosis of a disease through an image of living tissue. A diagnosis result generation system includes a marking information generation module for generating marking information indicating a result obtained by diagnosing whether a disease is present in biological tissue provided on a slide of which a biological image is obtained therefrom, wherein the marking information includes disease state information for each pixel of the biometric image obtained from the slide; a contour extraction module for extracting at least one contour from the marking information; and a machine-readable/human-readable generation module for generating a machine-readable/human-readable document including outline information of each of the at least one extracted contour.
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公开(公告)号：US20240153073A1
公开(公告)日：2024-05-09
申请号：US18280662
申请日：2022-03-07
Applicant: DEEP BIO INC.
Inventor： Joon Young CHO , Hye yoon CHANG , Tae Yeong Kwak , Sun woo KIM
CPC classification number: G06T7/0012 , G16H30/20 , G16H50/20 , G06T2207/20081 , G06T2207/20084 , G06T2207/30081 , G06T2207/30096
Abstract: A method for training an artificial neural network for detecting a prostate cancer from a TURP pathological image, includes: acquires a plurality of acquiring pathological images for primary training, each being a prostate needle biopsy pathological image or a radical prostatectomy pathological image; using the pathological images to primarily train an artificial neural network for determining prostate cancer; acquiring TURP pathological images; and using the TURP pathological images to secondarily train the primarily trained artificial neural network, wherein each TURP pathological image includes a non-prostate tissue region and/or a cauterized prostate tissue region, and does not include any prostate cancer lesion region.
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公开(公告)号：US20230298753A1
公开(公告)日：2023-09-21
申请号：US18017397
申请日：2020-07-23
Applicant: DEEP BIO INC.
Inventor： Sang Hun LEE , Sun Woo KIM
CPC classification number: G16H50/20 , G16H50/70 , G16H30/40 , G06T7/0014 , G06T2207/30024 , G06T2207/20081 , G06T2207/20084 , G06T2207/30081
Abstract: Disclosed in the present invention are a method for annotating a pathogenic site of a disease by means of semi-supervised learning. According to an aspect of the present invention, provided is the method comprising the steps in which: the diagnosis system using a neural network generates a patch-level classification neural network, which predicts a classification result relating to whether or not a predetermined disease is present in a patch, and a pixel-level classification neural network which predicts a classification result relating to the disease for each pixel constituting the patch; the diagnosis system obtains a plurality of slide images for learning, wherein each of the plurality of slide images for learning is labeled with a corresponding slide-level diagnosis result; and the diagnosis system gradually learns the patch-level classification neural network and pixel-level classification neural network by means of the plurality of slide images for learning.
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公开(公告)号：US20230298752A1
公开(公告)日：2023-09-21
申请号：US18017382
申请日：2020-07-23
Applicant: DEEP BIO INC.
Inventor： Sun Woo KIM
IPC: G16H50/20
CPC classification number: G16H50/20
Abstract: Disclosed are a method and a system for providing a customized diagnosis system. A method for providing a diagnosis system, which is to achieve the technical task, comprises the steps wherein: a reference diagnosis system transmits, to a first diagnostic subject system, M (M is an integer of 2 or larger) pieces of unannotated learning data for customization; the reference diagnosis system receives the M pieces of learning data for customization, which are annotated by a first diagnostic subject side, from the first diagnostic subject system; and the received annotated M pieces of learning data for customization are reflected in the reference diagnosis system, wherein the M pieces of learning data for customization, which have been annotated by the first diagnostic subject side, are reflected in a neural network for the reference diagnosis system under a condition that a predetermined price is to be paid.
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Patent Agency Ranking
公开(公告)号：US20230229927A1
公开(公告)日：2023-07-20
申请号：US18008426
申请日：2021-06-03
Applicant: DEEP BIO INC.
Inventor： Sun Woo KIM , Tae Yeong KWAK , Hye Yoon CHANG , Ye Chan MUN
IPC: G06N3/09 , G06N3/0455 , G06T7/00
CPC classification number: G06N3/09 , G06N3/0455 , G06T7/0012 , G06T2207/30096 , G06T2207/20081 , G06T2207/20084 , G06T2207/20076 , G06T2207/20021
Abstract: The present disclosure discloses a method and system for training a neural network for determining severity, and more particularly, a method and system which may effectively learn a neural network performing patch unit severity diagnosis using a pathological slide image to which a severity indication (label) is given.
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公开(公告)号：US20210391076A1
公开(公告)日：2021-12-16
申请号：US17282775
申请日：2019-10-04
Applicant: DEEP BIO INC.
Inventor： Tae Yeong KWAK , Sang Hun LEE , Sun Woo KIM
Abstract: A system for searching for a pathological image includes: an autoencoder having an encoder for receiving an original pathological image and extracting a feature of the original pathological image, and a decoder for receiving the feature of the original pathological image extracted by the encoder and generating a reconstructed pathological image corresponding to the original pathological image; a diagnostic neural network for receiving the reconstructed pathological image generated by the autoencoder that has received the original pathological image, and outputting a diagnosis result of a predetermined disease; and a training module for training the autoencoder and the diagnostic neural network by inputting a plurality of training pathological images, each labeled with a diagnosis result, into the autoencoder. The autoencoder is trained by reflecting the diagnosis result of the reconstructed pathological image output from the diagnostic neural network.
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公开(公告)号：US20210304889A1
公开(公告)日：2021-09-30
申请号：US15734907
申请日：2019-06-04
Applicant: DEEP BIO INC.
Inventor： Sanghun LEE , JoonYoung CHO , Sun Woo KIM
Abstract: A two-phase disease diagnosis system includes a processor and a storage device storing a neural network, and using a slide including a biometric image and the neural network. The system includes a patch neural network that receives, through an input layer, a given patch segmented in a given size from the slide and outputs patch level diagnosis results indicating whether a disease is present in the patch and a slide diagnosis engine that marks a patch determined to be cancer based on the patch level diagnosis results for each of multiple patches included in the slide and outputs slide level diagnosis results indicating whether a disease is present in the slide based on the marked results. The patch neural network receives, through the input layer, four-channel information including original color information three-channels and a gray channel for the patch.
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公开(公告)号：US20210295997A1
公开(公告)日：2021-09-23
申请号：US17266119
申请日：2019-08-07
Applicant: DEEP BIO INC.
Inventor： Sun Woo KIM
Abstract: A biometric image diagnosis system includes a first search module that retrieves, from a database (DB) storing pre-diagnosed biometric images and results with regards to the pre-diagnosed biometric images, at least one similar biometric image having similar characteristics to a predetermined biometric image to be diagnosed; a second search module configured to retrieve at least one pre-diagnosed biometric image pre-diagnosed by a diagnostician from the DB; a communications module that transmits the at least one similar biometric image and the at least one pre-diagnosed biometric image to a terminal of the diagnostician; and a storage module that stores, in the DB, a disease diagnosis result with respect to the biometric image to be diagnosed, wherein the terminal of the diagnostician displays the biometric, image to be diagnosed, the at least one similar biometric image, and the at least one pre-diagnosed biometric image.
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公开(公告)号：US20210295509A1
公开(公告)日：2021-09-23
申请号：US17266103
申请日：2019-08-07
Applicant: DEEP BIO INC.
Inventor： Sang Hun LEE , Sun Woo KIM
Abstract: A disease diagnosis system implemented in a system including a processor and which uses the neural network and a slide on which biological tissue is provided and from which a biological image is obtained, the system including: a pre-processing module for generating first to Kth image information corresponding to each of a plurality of patches obtained by dividing the slide into a predetermined size, wherein ith image information, 1
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