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公开(公告)号：US20220301156A1
公开(公告)日：2022-09-22
申请号：US17591758
申请日：2022-02-03
Inventor： Zhenghan Fang , Junjie Bai , Youbing Yin , Xinyu Guo , Qi Song
Abstract: Embodiments of the disclosure provide systems and methods for analyzing medical images using a learning model. The system receives a medical image acquired by an image acquisition device. The system may additionally include at least one processor configured to apply the learning model to perform an image analysis task on the medical image. The learning model is trained jointly with an error estimator using training images comprising a first set of labeled images and a second set of unlabeled images. The error estimator is configured to estimate an error of the learning model associated with performing the image analysis task.
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12.发明申请公开(公告)号：US20220215956A1
公开(公告)日：2022-07-07
申请号：US17567486
申请日：2022-01-03
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Bin Kong , Youbing Yin , Xin Wang , Yi Lu , Qi Song
IPC: G16H50/20 , G06N5/04 , G06V10/774 , G06T7/00
Abstract: The disclosure relates to a system and method for predicting physiological-related parameters based on a medical image. The method includes receiving a medical image acquired by an image acquisition device and predicting a sequence of physiological-related parameters at a sequence of positions and simultaneously estimating an uncertainty level of the predicted sequence of physiological parameters from the medical image by using a sequential learning model. The sequential learning model is trained to minimize a loss function associated with the uncertainty level. The method not only provides predictions but also the corresponding uncertainty estimations by using sequential learning model(s), thus improving the transparency and explainability of the sequential learning model.
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公开(公告)号：US11357464B2
公开(公告)日：2022-06-14
申请号：US17317487
申请日：2021-05-11
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Bin Kong , Yi Lu , Junjie Bai , Zhenghan Fang , Qi Song
IPC: G06T7/00 , A61B6/03 , G16H50/20 , A61B6/00 , A61B5/00 , G16H10/60 , G06N3/04 , G06N3/08 , G16H30/40 , A61B6/02
Abstract: Embodiments of the disclosure provide methods and systems for determining a disease condition from a 3D image of a patient. The exemplary system may include a communication interface configured to receive the 3D image acquired of the patient by an image acquisition device. The system may further include a processor, configured to determine a 3D region of interest from the 3D image and apply a detection network to the 3D region of interest to determine the disease condition and a severity of the disease condition. The detection network is a multi-task learning network that determines the disease condition based on one or more lesion masks determined from the 3D region of interest and determines the severity of the disease condition from the 3D region of interest. The processor is further configured to provide a diagnostic output based on the disease condition and the severity of the disease condition.
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公开(公告)号：US10803583B2
公开(公告)日：2020-10-13
申请号：US16056535
申请日：2018-08-07
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Kunlin Cao , Yuwei Li , Junjie Bai , Xiaoyang Xu
Abstract: The disclosure relates to systems and methods for determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.
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15.发明申请公开(公告)号：US20200311485A1
公开(公告)日：2020-10-01
申请号：US16827613
申请日：2020-03-23
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Qi Song , Junjie Bai , Yi Lu , Yi Wu , Feng Gao , Kunlin Cao
Abstract: Methods and Systems for generating a centerline for an object in an image and computer readable medium are provided. The method includes receiving an image containing the object. The method also includes generating the centerline of the object by tracing a sequence of patches with a virtual agent. For each patch other than the initial patch, the method determines a current patch based on the position and action of the virtual agent at a previous patch. The method further determines a policy function and a value function based on the current patch using a trained learning network, which includes an encoder followed by a first learning network and a second learning network. The learning network is trained by maximizing a cumulative reward. The method also determines the action of the virtual agent at the current patch. Additionally, the method displays the centerline of the object.
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Patent Agency Ranking
16.发明申请公开(公告)号：US20190050982A1
公开(公告)日：2019-02-14
申请号：US16028389
申请日：2018-07-05
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Qi Song , Shanhui Sun , Feng Gao , Junjie Bai , Hanbo Chen , Youbing Yin
Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.
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公开(公告)号：US11748902B2
公开(公告)日：2023-09-05
申请号：US17330557
申请日：2021-05-26
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Junjie Bai , Zhihui Guo , Youbing Yin , Xin Wang , Yi Lu , Kunlin Cao , Qi Song , Xiaoyang Xu , Bin Ouyang
CPC classification number: G06T7/68 , G06N3/045 , G06N3/08 , G06T2207/20081 , G06T2207/20084 , G06T2207/30101 , G06T2207/30172
Abstract: Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes detecting at least one bifurcation of the object using a trained bifurcation learning network based on the image. The method further includes extracting the centerline of the object based on a constraint condition that the centerline passes through the detected bifurcation.
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公开(公告)号：US20230098121A1
公开(公告)日：2023-03-30
申请号：US17489682
申请日：2021-09-29
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Feng Gao , Hao-Yu Yang , Yue Pan , Youbing Yin , Qi Song
IPC: G16H50/20 , G16H10/60 , G16H50/30 , G16H50/50 , G16H30/40 , G06F16/23 , A61B5/00 , A61B5/02 , A61B5/107
Abstract: The disclosure relates to a method for prognosis management based on medical information of a patient, a device, and a medium. The method includes acquiring, by a processor, medical information of the patient at a first time. The method further includes receiving the medical information of the patient at a first time. The method may further include predicting, by a processor, a progression condition of an object associated with the patient at a second time based on the acquired medical information of the first time. The progression condition is indicative of a prognosis risk, and the second time is after the first time. The method may also include outputting the predicted progression condition to an information management system. The method is helpful for users to understand the potential prognosis risk of the object at the second time to aid users in making treatment decisions.
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19.发明申请公开(公告)号：US20220198226A1
公开(公告)日：2022-06-23
申请号：US17692337
申请日：2022-03-11
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Qi Song , Junjie Bai , Yi Lu , Yi Wu , Feng Gao , Kunlin Cao
Abstract: Methods and Systems for generating a centerline for an object in an image and computer readable medium are provided. The method includes receiving an image containing the object. The method also includes generating the centerline of the object, by a processor, using a reinforcement learning network configured to predict movement of a virtual agent that traces the centerline in the image. The reinforcement learning network is further configured to perform at least one auxiliary task that detects a bifurcation in a trajectory of the object. The reinforcement learning network is trained by maximizing a cumulative reward and minimizing an auxiliary loss of the at least one auxiliary task. Additionally, the method includes displaying the centerline of the object.
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20.发明授权公开(公告)号：US11341631B2
公开(公告)日：2022-05-24
申请号：US16028389
申请日：2018-07-05
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Qi Song , Shanhui Sun , Feng Gao , Junjie Bai , Hanbo Chen , Youbing Yin
Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.
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