Patents by Inventor Youbing YIN
Youbing YIN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 11495357Abstract: The present disclosure is directed to a method and system for automatically predicting a physiological parameter based on images of vessel. The method includes receiving the images of a vessel acquired by an imaging device. The method further includes determining a sequence of temporal features at a sequence of positions on a centerline of the vessel based on the images of the vessel, and determining a sequence of structure-related features at the sequence of positions on the centerline of the vessel. The method also includes fusing the sequence of structure-related features and the sequence of temporal features at the sequence of positions respectively. The method additionally includes determining the physiological parameter for the vessel at the sequence of positions, by using a sequence-to-sequence neural network configured to capture sequential dependencies among the sequence of fused features.Type: GrantFiled: November 30, 2020Date of Patent: November 8, 2022Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Bin Ma, Ying Xuan Zhi, Xiaoxiao Liu, Xin Wang, Youbing Yin, Qi Song
-
Publication number: 20220351863Abstract: This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure.Type: ApplicationFiled: April 21, 2022Publication date: November 3, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Xin WANG, Youbing YIN, Bin KONG, Yi LU, Hao-Yu YANG, Xinyu GUO, Qi SONG
-
Publication number: 20220351374Abstract: This disclosure discloses a method for analyzing clinical data. The Method includes extracting a first feature information by applying a neural network to the clinical data; predicting a disease status related parameter by applying a regression model to the extracted first feature information; generating a second feature information based on the extracted first feature information and the disease status related parameter; and predicting a disease status classification result by applying a classification model to the second feature information. The method can improve the prediction accuracy and the diagnosis efficiency of doctors.Type: ApplicationFiled: April 20, 2022Publication date: November 3, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Xin WANG, Youbing YIN, Bin KONG, Yi LU, Xinyu GUO, Hao-Yu YANG, Qi SONG
-
Publication number: 20220344033Abstract: The present disclosure relates to a method and a system for generating anatomical labels of an anatomical structure. The method includes receiving an anatomical structure with an extracted centerline, or a medical image containing the anatomical structure with the extracted centerline; and predicting the anatomical labels of the anatomical structure based on the centerline of the anatomical structure, by utilizing a trained deep learning network. The deep learning network includes a branched network, a Graph Neural Network, a Recurrent Neural Network and a Probability Graph Model, which are connected sequentially in series. The branched network includes at least two branch networks in parallel. The method in the disclosure can automatically generate the anatomical labels of the whole anatomical structure in medical image end to end and provide high prediction accuracy and reliability.Type: ApplicationFiled: April 21, 2022Publication date: October 27, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Xin WANG, Youbing YIN, Bin KONG, Yi LU, Xinyu GUO, Hao-Yu YANG, Junjie BAI, Qi SONG
-
Patent number: 11462326Abstract: A method and system can be used for disease quantification modeling of an anatomical tree structure. The method may include obtaining a centerline of an anatomical tree structure and generating a graph neural network including a plurality of nodes based on a graph. Each node corresponds to a centerline point and edges are defined by the centerline, with an input of each node being a disease related feature or an image patch for the corresponding centerline point and an output of each node being a disease quantification parameter. The method also includes obtaining labeled data of one or more nodes, the number of which is less than a total number of the nodes in the graph neural network. Further, the method includes training the graph neural network by transferring information between the one or more nodes and other nodes based on the labeled data of the one or more nodes.Type: GrantFiled: June 19, 2020Date of Patent: October 4, 2022Assignee: KEYA MEDICAL TECHNOLOGY CO., LTD.Inventors: Xin Wang, Youbing Yin, Junjie Bai, Qi Song, Kunlin Cao, Yi Lu, Feng Gao
-
Publication number: 20220301156Abstract: 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.Type: ApplicationFiled: February 3, 2022Publication date: September 22, 2022Applicant: Shenzhen Keya Medical Technology CorporationInventors: Zhenghan Fang, Junjie Bai, Youbing Yin, Xinyu Guo, Qi Song
-
Publication number: 20220284571Abstract: Embodiments of the disclosure provide systems and methods for medical image analysis. A method may include receiving a medical image acquired of a subject by an image acquisition device. The method may also include applying a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image. The method may further include applying a score regression learning model to the at least one calcium region to determine a calcium score for the medical image. The method may additionally include providing the determined calcium score of the medical image for a diagnosis of the subject.Type: ApplicationFiled: October 20, 2021Publication date: September 8, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Hao-Yu Yang, Junjie Bai, Youbing Yin, Qi Song
-
Patent number: 11416994Abstract: Embodiments of the disclosure provide systems and methods for biomedical image analysis. A method may include receiving a plurality of unannotated biomedical images, including a first image and a second image. The method may also include determining that the first image is in a first view and the second image is in a second view. The method may further include assigning the first image to a first processing path for the first orientation. The method may additionally include assigning the second image to a second processing path for the second view. The method may also include processing the first image in the first processing path in parallel with processing the second image in the second processing path. The first path may share processing parameters with the second path. The method may further include providing a diagnostic output based on the processing of the first image and the second image.Type: GrantFiled: April 29, 2020Date of Patent: August 16, 2022Assignee: KEYAMED NA, INC.Inventors: Feng Gao, Hao-Yu Yang, Youbing Yin, Yue Pan, Xin Wang, Junjie Bai, Yi Wu, Kunlin Cao, Qi Song
-
Publication number: 20220215535Abstract: Embodiments of the disclosure provide methods and systems for joint abnormality detection and physiological condition estimation from a medical image. The exemplary method may include receiving, by at least one processor, the medical image acquired by an image acquisition device. The medical image includes an anatomical structure. The method may further include applying, by the at least one processor, a joint learning model to determine an abnormality condition and a physiological parameter of the anatomical structure jointly based on the medical image. The joint learning model satisfies a predetermined constraint relationship between the abnormality condition and the physiological parameter.Type: ApplicationFiled: January 3, 2022Publication date: July 7, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Bin Kong, Youbing Yin, Xin Wang, Yi Lu, Haoyu Yang, Junjie Bai, Qi Song
-
Publication number: 20220215958Abstract: The present disclosure relates to training methods for a machine learning model for physiological analysis. The training method may include receiving training data including a first dataset of labeled data of a physiological-related parameter and a second dataset of weakly-labeled data of the physiological-related parameter. The training method further includes training, by at least one processor, an initial machine learning model using the first dataset, and applying, by the at least one processor, the initial machine learning model to the second dataset to generate a third dataset of pseudo-labeled data of the physiological-related parameter. The training method also includes training, by the at least one processor, the machine learning model based on the first dataset and the third dataset, and providing the trained machine learning model for predicting the physiological-related parameter.Type: ApplicationFiled: January 4, 2022Publication date: July 7, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Bin Kong, Youbing Yin, Xin Wang, Yi Lu, Haoyu Yang, Junjie Bai, Qi Song
-
Publication number: 20220215534Abstract: Embodiments of the disclosure provide systems and methods for analyzing a medical image containing a vessel structure using a sequential model. An exemplary system includes a communication interface configured to receive the medical image and the sequential model. The sequential model includes a vessel extraction sub-model and a lesion analysis sub-model. The vessel extraction sub-model and the lesion analysis sub-model are independently or jointly trained. The exemplary system also includes at least one processor configured to apply the vessel extraction sub-model on the received medical image to extract location information of the vessel structure. The at least one processor also applies the lesion analysis sub-model on the received medical image and the location information extracted by the vessel extraction sub-model to obtain a lesion analysis result of the vessel structure. The at least one processor further outputs the lesion analysis result of the vessel structure.Type: ApplicationFiled: December 21, 2021Publication date: July 7, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Junjie Bai, Hao-Yu Yang, Youbing Yin, Qi Song
-
Publication number: 20220215956Abstract: 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.Type: ApplicationFiled: January 3, 2022Publication date: July 7, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Bin Kong, Youbing Yin, Xin Wang, Yi Lu, Qi Song
-
Publication number: 20220198226Abstract: 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.Type: ApplicationFiled: March 11, 2022Publication date: June 23, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Xin Wang, Youbing Yin, Qi Song, Junjie Bai, Yi Lu, Yi Wu, Feng Gao, Kunlin Cao
-
Patent number: 11357464Abstract: 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.Type: GrantFiled: May 11, 2021Date of Patent: June 14, 2022Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Xin Wang, Youbing Yin, Bin Kong, Yi Lu, Junjie Bai, Zhenghan Fang, Qi Song
-
Patent number: 11361440Abstract: Embodiments of the disclosure provide methods and systems for disease condition prediction from images of a patient. The system may include a communication interface configured to receive a sequence of images acquired of the patient by an image acquisition device. The sequence of images are acquired at a sequence of prior time points during progression of a disease. The system may include a processor, configured to determine regions of interest based on the sequence of images. The processor applies a progressive condition prediction network to the regions of interest to predict a level of disease progression at a future time point during the progression of the disease. The progressive condition prediction network predicts the level of disease progression based on the regions of interest and disease conditions at the sequence of prior time points. The processor further provides a diagnostic output based on the predicted level of disease progression.Type: GrantFiled: May 12, 2021Date of Patent: June 14, 2022Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Xin Wang, Youbing Yin, Bin Kong, Yi Lu, Junjie Bai, Zhenghan Fang, Qi Song
-
Patent number: 11341631Abstract: 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.Type: GrantFiled: July 5, 2018Date of Patent: May 24, 2022Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Qi Song, Shanhui Sun, Feng Gao, Junjie Bai, Hanbo Chen, Youbing Yin
-
Patent number: 11304756Abstract: Systems and methods for displaying a predicted outcome of a lung volume reduction procedure for a patient including a user interface, a processor, and programing operable on the processor for displaying a predicted outcome of the bronchoscopic lung volume reduction procedure on the user interface, wherein displaying the predicted outcome of the lung volume reduction procedure includes receiving patient data comprising volumetric images of the patient, analyzing the volumetric images to identify one or more features correlated to treatment outcome prediction, predicting an outcome for a treatment modality or treatment device using the one or more identified features, and displaying the predicted outcome on the user interface.Type: GrantFiled: March 2, 2019Date of Patent: April 19, 2022Assignee: VIDA Diagnostics, Inc.Inventors: Philippe Raffy, Youbing Yin
-
Patent number: 11308362Abstract: 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.Type: GrantFiled: March 23, 2020Date of Patent: April 19, 2022Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Xin Wang, Youbing Yin, Qi Song, Junjie Bai, Yi Lu, Yi Wu, Feng Gao, Kunlin Cao
-
Publication number: 20220091568Abstract: The disclosure relates to a method and device for predicting a physical parameter based on input physical information, and medium. The method may include predicting, by a processor, an intermediate variable based on the input physical information with an intermediate sub-model, which incorporates a constraint on the intermediate variable according to prior information of the physical parameter. The method may also include transforming, by the processor, the intermediate variable predicted by the intermediate sub-model to the physical parameter with a transformation sub-model.Type: ApplicationFiled: September 7, 2021Publication date: March 24, 2022Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATIONInventors: Bin Kong, Youbing Yin, Xin Wang, Yi Lu, Qi Song
-
Publication number: 20220067935Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition based on the medical image and automatically generate text information describing the medical condition. The at least one processor is further configured to construct the diagnosis report, where the diagnosis report includes at least one image view showing the medical condition and a report view including the text information describing the medical condition. The system also includes a display configured to display the diagnosis report.Type: ApplicationFiled: November 9, 2021Publication date: March 3, 2022Applicant: KEYA MEDICAL TECHNOLOGY CO., LTD.Inventors: Qi Song, Hanbo Chen, Zheng Te, Youbing Yin, Junjie Bai, Shanhui Sun