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公开(公告)号：US12094188B2
公开(公告)日：2024-09-17
申请号：US17565274
申请日：2021-12-29
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Junhuan Li , Ruoping Li , Ling Hou , Pengfei Zhao , Yuwei Li , Kunlin Cao , Qi Song
IPC: G06V10/774 , G06T7/00 , G06V10/776 , G06V10/82
CPC classification number: G06V10/7747 , G06T7/0012 , G06V10/776 , G06V10/82 , G06T2207/10081 , G06T2207/30048
Abstract: The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set. The present disclosure can evaluate and train the learning network in stages, therefore, the complexity of medical image processing is reduced, and the efficiency and accuracy of medical image analysis are improved.
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2.发明授权公开(公告)号：US11847547B2
公开(公告)日：2023-12-19
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
CPC classification number: G06N3/006 , G06F18/217 , G06N3/08 , G06T19/00 , G06V10/42 , G06V10/776
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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公开(公告)号：US11574122B2
公开(公告)日：2023-02-07
申请号：US16544837
申请日：2019-08-19
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Feng Gao , Changsheng Liu , Yue Pan , Youbing Yin , Kunlin Cao , Qi Song
IPC: G06F40/295 , G06N3/08 , G16H10/60
Abstract: Embodiments of the disclosure provide systems and methods for processing unstructured texts in a medical record. A disclosed system includes at least one processor configured to determine a plurality of word representations of an unstructured text and tag entities in the unstructured text by performing a named entity recognition task on the plurality of word representations. The at least one processor is further configured to determine position embeddings based on positions of words in the unstructured text relative to positions of the tagged entities and concatenate the plurality of word representations with the position embeddings. The at least one processor is also configured to determine relation labels between pairs of tagged entities by performing a relationship extraction task on the concatenated word representations and position embeddings.
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公开(公告)号：US11538161B2
公开(公告)日：2022-12-27
申请号：US17015070
申请日：2020-09-08
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 evaluating a blood vessel. The method includes receiving image data of the blood vessel acquired by an image acquisition device, and predicting, by a processor, blood vessel condition parameters of the blood vessel by applying a deep learning model to the acquired image data of the blood vessel. The deep learning model maps a sequence of image patches on the blood vessel to blood vessel condition parameters on the blood vessel, where in the mapping the entire sequence of image patches contribute to the blood vessel condition parameters. The method further includes providing the blood vessel condition parameters of the blood vessel for evaluating the blood vessel.
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公开(公告)号：US20220366679A1
公开(公告)日：2022-11-17
申请号：US17565274
申请日：2021-12-29
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Junhuan Li , Ruoping LI , Ling Hou , Pengfei Zhao , Yuwei Li , Kunlin Cao , Qi Song
IPC: G06V10/774 , G06V10/776 , G06T7/00 , G06V10/82
Abstract: The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set. The present disclosure can evaluate and train the learning network in stages, therefore, the complexity of medical image processing is reduced, and the efficiency and accuracy of medical image analysis are improved.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20200065374A1
公开(公告)日：2020-02-27
申请号：US16544837
申请日：2019-08-19
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Feng Gao , Changsheng Liu , Yue Pan , Youbing Yin , Kunlin Cao , Qi Song
Abstract: Embodiments of the disclosure provide systems and methods for processing unstructured texts in a medical record. A disclosed system includes at least one processor configured to determine a plurality of word representations of an unstructured text and tag entities in the unstructured text by performing a named entity recognition task on the plurality of word representations. The at least one processor is further configured to determine position embeddings based on positions of words in the unstructured text relative to positions of the tagged entities and concatenate the plurality of word representations with the position embeddings. The at least one processor is also configured to determine relation labels between pairs of tagged entities by performing a relationship extraction task on the concatenated word representations and position embeddings.
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公开(公告)号：US12026877B2
公开(公告)日：2024-07-02
申请号：US17482901
申请日：2021-09-23
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Jinchen Li , Guang Li , Chengwei Sun , Kunlin Cao , Qi Song
IPC: G06T7/00 , G06F18/2431 , G06N3/04 , G16H30/40
CPC classification number: G06T7/0012 , G06F18/2431 , G06N3/04 , G16H30/40 , G06T2207/10016 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30061
Abstract: The present disclosure provides a method, a device, and a non-transitory computer-readable storage medium for detecting a medical condition of an organ. The method includes obtaining 2D image sequences of the organ in a plurality of different directions and applying a plurality of classification branches to the 2D image sequences. Each classification branch receives a 2D image sequence of one direction and provides a classification result with respect to that direction. Each classification branch includes a convolutional neural network configured to extract first image features from the corresponding 2D image sequence and a recurrent neural network configured to extract second image features from the first image features. The method further includes fusing the classification results provided by the plurality of classification branches for detecting the medical 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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9.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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公开(公告)号：US11494908B2
公开(公告)日：2022-11-08
申请号：US17408321
申请日：2021-08-20
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Ruoping Li , Pengfei Zhao , Junhuan Li , Bin Ouyang , Yuwei Li , Kunlin Cao , Qi Song
Abstract: The present disclosure relates to a medical image analysis method, a medical image analysis device, and a computer-readable storage medium. The medical image analysis method includes receiving a medical image acquired by a medical imaging device; determining a navigation trajectory by performing navigation processing on the medical image based on an analysis requirement, the analysis requirement indicating a disease to be analyzed; extracting an image block set along the navigation trajectory; extracting image features using a first learning network based on the image block set; and determining an analysis result using a second learning network based on the image features and the navigation trajectory.
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