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11.发明公开公开(公告)号：US20230358835A1
公开(公告)日：2023-11-09
申请号：US18219678
申请日：2023-07-09
Applicant: ZHEJIANG LAB
Inventor： Jingsong LI , Ziyang CHEN , Wenyuan QIU , Qiqi TONG , Tianshu ZHOU
IPC: G01R33/56 , G06N3/0455 , G06N3/0464
CPC classification number: G01R33/5608 , G06N3/0455 , G06N3/0464
Abstract: The application discloses a variational autoencoder-based magnetic resonance weighted image synthesis method and device. The method includes the following steps: step S1: acquiring a multi-contrast real magnetic resonance weighted image and a magnetic resonance quantitative parametric image by using a magnetic resonance scanner; step S2: composing a magnetic resonance weighted image; step S3: constructing a pre-trained variational autoencoder model with an encoder-and-decoder structure; step S4: obtaining a variational autoencoder model; and step S5: synthesizing the magnetic resonance weighted image and the magnetic resonance quantitative parametric image into a second magnetic resonance weighted image by the variational auto-encoder model. In the application, the variational auto-encoder model is configured to obtain a proximate contrast information continuous distribution by training of the multi-contrast magnetic resonance weighted image, such that the variational autoencoder model involved in the application can be reconstructed to obtain magnetic resonance weighted images that are not present in training data.
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12.发明公开公开(公告)号：US20240161251A1
公开(公告)日：2024-05-16
申请号：US18489876
申请日：2023-10-19
Applicant: ZHEJIANG LAB
Inventor： Jingsong LI , Jinnan HU , Peijun HU , Yu TIAN , Tianshu ZHOU
CPC classification number: G06T5/002 , G06T5/10 , G06T2207/20064 , G06T2207/20081 , G06T2207/20084
Abstract: Disclosed is an image denoising method and apparatus based on wavelet high-frequency channel synthesis. Image data are expanded to a plurality of frequency-domain channels, a plurality of “less-noise” channels and a plurality of “more-noise” channels are grouped through a noise-sort algorithm, and a denoising submodule and a synthesis submodule based on style transfer are combined to form a generative network. A discriminative network is established to add a constraint to the global loss function. After iteratively training the GAN model described above, the denoised image data can be obtained through wavelet inverse transformation. The disclosed algorithm can effectively solve the problem of “blurring” and “loss of details” introduced by traditional filtering or CNN-based deep learning methods, which is especially suitable for noise-overwhelmed image data or high dimensional image data.
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公开(公告)号：US20240061993A1
公开(公告)日：2024-02-22
申请号：US18360794
申请日：2023-07-27
Applicant: ZHEJIANG LAB
Inventor： Tianshu ZHOU , Xin GAO , Jingsong LI , Yu TIAN
CPC classification number: G06F40/14 , G10L15/22 , G10L2015/223
Abstract: The present disclosure discloses a multi-component abstract association and fusion method and apparatus in page design. The method includes the following steps: step S1: a construction demand is acquired, and the construction demand is analyzed through a speech recognition method to obtain a natural language text; step S2: an abstract model is constructed by predefining a component library, a rule library and a relationship library, and the abstract model performs components fusion to obtain a JSON structure of a fused component; step S3: the JSON structure of the fused component is escaped into a virtual DOM by using a rendering function, and attributes and events of a virtual DOM node are mapped to obtain a fused component drawing result; and step S4: a real DOM structure is created and interpolated into a real DOM node, so as to realize display of the fused component on a view.
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14.发明公开公开(公告)号：US20240047051A1
公开(公告)日：2024-02-08
申请号：US18353865
申请日：2023-07-17
Applicant: ZHEJIANG LAB
Inventor： Jingsong LI , Jing XIA , Yinghao ZHAO , Yu TIAN , Tianshu ZHOU
Abstract: Disclosed is an emergency blood dispatching method and system based on early prediction and unmanned fast delivery. In the present disclosure, an emergency blood use prediction model and an unmanned aerial vehicle fast delivery route are introduced, blood use demands of pre-hospital emergency trauma patients are accurately predicted, pre-hospital emergency blood transfusion of patients is achieved through unmanned aerial vehicle sites, it does not need to consume a lot of road traffic time to arrive at a hospital and then starts blood transfusion, the speed of blood supply and treatment quality of the patients with massive traumatic hemorrhage are improved, and it is of great value to rescue remote mountain trauma patients. The present disclosure evaluates blood use demands of the hospital in real time, and combines an unmanned aerial vehicle and a blood delivery car to fast deliver needed blood products from a blood center to the hospital.
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15.发明公开公开(公告)号：US20240029846A1
公开(公告)日：2024-01-25
申请号：US18362950
申请日：2023-07-31
Applicant: ZHEJIANG LAB
Inventor： Yu WANG , Jingsong LI , Yu TIAN , Tianshu ZHOU
Abstract: Disclosed is a method and a system for discovering new drug indications by fusing patient portrait information. According to the present disclosure, real-world patient medication and patient diagnostic data are introduced into a data-driven drug relocation solution, an actual use effect of drugs in a broader population is added into a new drug-disease relationship prediction model. According to the present disclosure, a patient portrait is constructed as a feature expression of patient information, and is used to construct a patient-patient network as a medium between drug and disease networks, and a heterogeneous network system corresponding to actual clinical processes is constructed. Prediction results in the present disclosure are more closely related to a clinical practice, and a probability of success in subsequent validation of old drugs for new usage and new clinical trials is greater.
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Patent Agency Ranking
公开(公告)号：US20220092789A1
公开(公告)日：2022-03-24
申请号：US17541271
申请日：2021-12-03
Applicant: ZHEJIANG LAB
Inventor： Jingsong LI , Peijun HU , Yu TIAN , Tianshu ZHOU
Abstract: The present invention discloses an automatic pancreas CT segmentation method based on a saliency-aware densely connected dilated convolutional neural network. Under a coarse-to-fine two-step segmentation framework, the method uses a densely connected dilated convolutional neural network as a basis network architecture to obtain multi-scale image feature expression of the target. An initial segmentation probability map of the pancreas is predicted in the coarse segmentation stage. A saliency map is then calculated through saliency transformation based on a geodesic distance transformation. A saliency-aware module is introduced into the feature extraction layer of the densely connected dilated convolutional neural network, and the saliency-aware densely connected dilated convolutional neural network is constructed as the fine segmentation network model. A coarse segmentation model and the fine segmentation model are trained using a training set, respectively.
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公开(公告)号：US20220092430A1
公开(公告)日：2022-03-24
申请号：US17541298
申请日：2021-12-03
Applicant: ZHEJIANG LAB
Inventor： Jingsong LI , Tianshu ZHOU , Ziyue YANG , Shengqiang CHI
Abstract: Provided is a time series deep survival analysis system combined with active learning. The system includes: a data collection module, an active learning module, and a time series deep survival analysis module; the data collection module is used for obtaining survival data of objects to be analyzed; combined with an active learning method, the active learning module selects a part of right censored data to label a survival time; and the time series deep survival analysis module constructs a time series deep survival analysis neural network model, and takes uncensored data and right censored data as model inputs, so as to obtain survival time prediction results of the objects to be analyzed. The present application can make full use of the right censored data in the survival data and time series features.
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18.发明申请公开(公告)号：US20240395408A1
公开(公告)日：2024-11-28
申请号：US18788009
申请日：2024-07-29
Applicant: ZHEJIANG LAB
Inventor： Jingsong LI , Peijun HU , Yu TIAN , Tianshu ZHOU
Abstract: A pancreatic postoperative diabetes prediction system based on supervised deep subspace learning. A deep convolutional neural network and the MITK software are used to obtain postoperative residual pancreas area, so as to taken as the region-of-interest. Traditional image radiomics features and deep semantic features are extracted from the residual pancreas area, and a high-dimensional image feature set is constructed. Clinical factors related to diabetes, including pancreatic excision rate, fat and muscle tissue components, demographic information and living habits are extracted, and a clinical feature set is constructed. Based on a supervised deep subspace learning network, image and clinical features are represented and fused in subspace in dimensionality reduction, while a prediction model is trained to mine sensitive features highly relevant to the prediction risk of a patient suffering postoperative diabetes mellitus with a high degree of automation and discriminative accuracy.
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公开(公告)号：US20240212862A1
公开(公告)日：2024-06-27
申请号：US18595379
申请日：2024-03-04
Applicant: ZHEJIANG LAB
Inventor： Jingsong LI , Feng WANG , Hang ZHANG , Shengqiang CHI , Yu TIAN , Tianshu ZHOU
IPC: G16H50/50 , G06N3/0475 , G06N5/022
CPC classification number: G16H50/50 , G06N3/0475 , G06N5/022
Abstract: Disclosed is a general multi-disease prediction system based on causal check data generation. For a general scenario, the present invention provides a tendency score calculation method based on a general tendency score network from the perspective of causality; compared with the problem of poor interpretability of traditional generative adversarial networks, the present invention provides a generative adversarial network based on causal check, so that generated data better conforms to real causal logic; in view of the problem that existing graph convolutional neural networks are modeled only from the perspective of correlation, the present invention provides a general multi-disease prediction model based on a general causal graph convolutional neural network, and a causal effect value is integrated to improve the prediction performance of the general multi-disease prediction system on diseases, thereby solving the problems of poor model performance and low robustness caused by few training samples in a general scenario.
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20.发明公开公开(公告)号：US20240184543A1
公开(公告)日：2024-06-06
申请号：US18525804
申请日：2023-11-30
Applicant: ZHEJIANG LAB
Inventor： Tianshu ZHOU , Xin GAO , Jingsong LI , Yu TIAN
Abstract: Disclosed is a page multiplexing method, a page multiplexing device, a storage medium and an electronic apparatus. After obtaining the page frame information of pages to be configured in a client to be developed, a component relational tree corresponding to the plurality of pages can be determined. The component relational tree is compared with a pre-constructed reference relational tree to determine a target tree structure composed of target components from the reference relational tree. Dependencies between target components in the reference relational tree match those in the component relational tree. The component code of the target component used by the developed client is queried to multiplex the component code. The component relational tree corresponding to pages to be developed can be compared with the reference relational tree corresponding to each page included in the developed client to determine the component code that can be multiplexed.
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