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公开(公告)号：US12062450B2
公开(公告)日：2024-08-13
申请号：US17626806
申请日：2020-07-10
Applicant: DEEP BIO INC.
Inventor： Sun Woo Kim , Joon Young Cho , Sang Hun Lee
CPC classification number: G16H50/20 , G06F18/24 , G06T7/0012 , G06T7/11 , G16H30/20 , G16H30/40 , G06V10/70
Abstract: A disease diagnosis system uses a slide of a biological image and the neural network, the disease diagnosis system including a patch-level segmentation neural network that receives, for each predetermined patch in which the slide is divided into a predetermined size, the patch as an input layer so as to specify the area in which the disease in the patch exists, wherein the patch-level segmentation neural network comprises: a patch-level classification neural network, which receives the patch as an input layer so as to output a patch-level classification result about whether the disease exists in the patch; and a patch-level segmentation architecture, which receives a feature map generated in each of two or more feature map extraction layers from among hidden layers included in the patch-level classification neural network, so as to specify the area in which the disease in the patch exists.
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22.发明公开公开(公告)号：US20240156416A1
公开(公告)日：2024-05-16
申请号：US18282202
申请日：2022-03-14
Applicant: DEEP BIO INC.
Inventor： Sun Woo KIM
CPC classification number: A61B5/7275 , A61B5/1079 , G06T7/0012 , G06T7/11 , G06T7/60 , G16H50/20 , G06T2207/20084 , G06T2207/30024 , G06T2207/30081
Abstract: A prognosis prediction method using a result of disease diagnosis through a neural network and a system therefor. The prognosis prediction method includes: receiving a biometric image as an input; generating an expression region diagnosis result in which an expression region in the biometric image, in which a disease has been expressed, is determined with respect to the input biometric image; and determining first information corresponding to the size of the entire tissue in the biometric image and second information corresponding to the size of the expression region in the biometric image, on the basis of the diagnosis result, and generating prognosis prediction information based on a result of the determination.
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23.发明公开公开(公告)号：US20240037855A1
公开(公告)日：2024-02-01
申请号：US18276039
申请日：2022-02-08
Applicant: DEEP BIO INC.
Inventor： Sun Woo KIM
CPC classification number: G06T19/00 , G16H30/40 , G16H50/20 , G06T2210/44 , G06T2219/004 , A61B10/0241
Abstract: A method for generating a three-dimensional prostate pathological image, and a system therefor are disclosed. The method for generating a three-dimensional prostate pathological image includes the steps of: by a system for generating a three-dimensional prostate pathological image, specifying a three-dimensional prostate image; by the system for generating a three-dimensional prostate pathological image, obtaining, through a diagnosis system, a digital diagnosis result for each of at least one specimen corresponding to predetermined template coordinates obtained through a transperineal template prostate biopsy (TTPB); and by the system for generating a three-dimensional prostate pathological image, displaying, on the three-dimensional prostate image, an onset site of prostate cancer existing in the at least one specimen on the basis of the template coordinates for each specimen and the digital diagnosis result.
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公开(公告)号：US11798686B2
公开(公告)日：2023-10-24
申请号：US17282775
申请日：2019-10-04
Applicant: DEEP BIO INC.
Inventor： Tae Yeong Kwak , Sang Hun Lee , Sun Woo Kim
CPC classification number: G16H50/20 , G06N3/045 , G06N3/088 , G06T7/0014 , G16H30/40 , G06F18/2148 , G06T2207/30081 , G06V2201/03
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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25.发明公开公开(公告)号：US20230306624A1
公开(公告)日：2023-09-28
申请号：US18010701
申请日：2021-06-16
Applicant: DEEP BIO INC.
Inventor： Ui Geo MUN , Min Ah CHO , Tae Yeong KWAK , Sun Woo KIM
CPC classification number: G06T7/60 , G06T7/0012 , G06T7/11 , G06V10/28 , G06V10/426 , G16H30/40 , G06T2207/20072 , G06T2207/30024 , G06T2207/30096
Abstract: Disclosed are a method for measuring the length of a living tissue included in a slide image, and a computing system for performing same. According to one aspect of the present invention, the method comprising the steps of: segmenting the slide image into a plurality of patches having a predetermined size; generating a graph corresponding to the slide image; for each edge included in the graph, setting a weight of the edge; for each connected component of the graph including two or more nodes, detecting shortest paths between all node pairs included in the connected components and determining a longest shortest path having the longest length from among the detected shortest paths between all the node pairs; and calculating the length of the living tissue included in the slide image, on the basis of the longest shortest path of each connected component constituting the graph.
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Patent Agency Ranking
公开(公告)号：US20230072274A1
公开(公告)日：2023-03-09
申请号：US17795546
申请日：2020-07-24
Applicant: DEEP BIO, INC.
Inventor： In Young PAIK , Sang Jun OH , Tae Yeong KWAK
Abstract: A neuron-level plasticity control (NPC) addresses the issue of catastrophic forgetting in an artificial neural network. The plasticity of a network is controlled at a neuron level rather than at a connection level during training of a new task, thereby conserving existing knowledge. The neuron-level plasticity control evaluates the importance of each neuron and applies a low training speed to integrate important neurons. In addition, a scheduled NPC (SNPC) is provided that uses training schedule information to more clearly protect important neurons.
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公开(公告)号：US20210407675A1
公开(公告)日：2021-12-30
申请号：US17294283
申请日：2019-11-18
Applicant: DEEP BIO INC.
Inventor： Sun Woo KIM
IPC: G16H50/20
Abstract: Disclosed are a supervised learning-based consensus diagnosis method and a system thereof. The supervised learning-based consensus diagnosis method includes: a step of confirming, by a consensus diagnostic system, N individual diagnosis results in which each of N (N is an integer of 2 or more) diagnostic systems receives and outputs predetermined biological data, wherein the N diagnostic systems, respectively, are systems that are each trained with learning data annotated by different annotation subjects; and a step of outputting a consensus diagnosis result of the biological data on the basis of the individual diagnosis results confirmed by the consensus diagnosis system.
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公开(公告)号：US20210248745A1
公开(公告)日：2021-08-12
申请号：US17271214
申请日：2019-08-07
Applicant: DEEP BIO INC.
Inventor： Joon Young CHO , Sun Woo KIM
Abstract: A system for diagnosing a disease, implemented in a system and which uses a slide of a biological image, and a neural network, includes a patch level segmentation neural network which, for each patch in which the slide is divided into a predetermined size, receives the patch through an input layer and specifies an area in which a disease exists in the patch, wherein the patch level segmentation neural network is provided with a disease diagnosis system including a patch level classification neural network which receives the patch through an input layer and outputs a patch level classification result regarding whether the disease exists in the patch, and a patch level segmentation architecture which receives a feature map generated in each of plural feature extraction layers among hidden layers included in the patch level classification neural network and specifies an area in which a disease exists in the patch.
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公开(公告)号：US11074686B2
公开(公告)日：2021-07-27
申请号：US16468173
申请日：2017-12-06
Applicant: DEEP BIO, INC.
Inventor： Sun Woo Kim
Abstract: A disease diagnosis system including a processor and a storage device for storing a neural network and using a biometric image and the neural network, the disease diagnosis system including a micro-neural network for receiving a first tile included in the biometric image through a first input layer, and including a plurality of first layers and an output layer, and a macro-neural network for receiving a macro-tile including the first tile and at least one or more second tiles adjacent to the first tile through a second input layer, and including a plurality of second layers and the output layer, in which the output layer includes at least one state channel indicating a state of a disease of a biological tissue corresponding to the first tile.
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公开(公告)号：US20210142900A1
公开(公告)日：2021-05-13
申请号：US16972231
申请日：2019-06-04
Applicant: DEEP BIO INC.
Inventor： Sanghun LEE , JoonYoung CHO , Sun Woo KIM
IPC: G16H50/20
Abstract: A disease diagnosis system includes a processor and a storage device storing a neural network. The processor trains the neural network in the storage device to output a determination value corresponding to a probability having at least one of a plurality of states using a given loss function and learning data labeled so that a given unitary unit included in a biometric image is to have at least one of the plurality of states. The neural network includes a specific layer to output a plurality of feature values corresponding to a probability that the unitary unit is to be determined as each of the plurality of states. The loss function incorporates both first and second feature values corresponding to first and second states into a dual labeling unitary unit with the first state having a higher probability and a second state having lower probability.
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