公开(公告)号：US20250132057A1

公开(公告)日：2025-04-24

申请号：US18600800

申请日：2024-03-11

Applicant: ZHEJIANG LAB

Inventor： Zenghui XU ,  Ji ZHANG ,  Yu ZHANG ,  Ting YU ,  Jin ZHAO ,  Linlin HOU ,  Zhan ZHANG

IPC: G16H50/80

Abstract: An infectious disease infection prediction method, an apparatus, and a storage medium based on macro-micrograph fusion are provided. The method includes: acquiring macrographs of a plurality of first regions and micrographs of second regions within a set period; inputting the macroscopic graphs and the microscopic graphs into two graph convolutional neural networks to obtain two hidden layer vectors respectively, and fusing the two hidden layer vectors to obtain fusion hidden layer information of the first regions; performing a time sequence calculation of the fusion hidden layer information to obtain time sequence hidden layer information of the first regions; inputting the time series hidden layer information into two prediction networks to obtain two prediction results, respectively, and performing fusion calculation of the two prediction results to obtain a final prediction result of infectious diseases in the first regions.

公开(公告)号：US20240273118A1

公开(公告)日：2024-08-15

申请号：US18472202

申请日：2023-09-21

Applicant: ZHEJIANG LAB

Inventor： Ting JIANG ,  Yu ZHANG ,  Ting YU ,  Ji ZHANG ,  Linlin HOU ,  Jin ZHAO

IPC: G06F16/28

CPC classification number: G06F16/285

Abstract: A data classification method and apparatus, a device and a storage medium. A structural feature of the respective node in graph data may be determined according to a neighbor node of the respective node in the graph data through a deviation between the decoded feature obtained by decoding the embedded coding feature of the respective node in the graph data and the initial feature of the respective node, and then the embedded coding feature corresponding to the respective node is adjusted according to the decoded feature of the respective node and the structural feature of the respective node in the graph data to obtain the adjusted feature corresponding to the respective node, so that accuracy of an obtained feature of the respective node is improved, and thus accuracy of data classification may be improved.

公开(公告)号：US20230225649A1

公开(公告)日：2023-07-20

申请号：US18125645

申请日：2023-03-23

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Chaoliang SUN ,  Zhichao WANG ,  Haotian QIAN ,  Jun LI ,  Jingsong LI

IPC: A61B5/16 ,  A61B5/00

CPC classification number: A61B5/16 ,  A61B5/7264

Abstract: Disclosed is a graph model-based brain functional alignment method. The method includes: mapping high-dimensional functional brain imaging data to a two-dimensional time-series matrix by taking brain functional activity signals of a subject under a specific cognitive function state as input , constructing a model based on graph convolutional networks to distinguish different cognitive function states, generating a brain activation distribution priori graph by a meta analysis method to assist in predicting a specific brain function activation mode of each subject, combining the two to map functional brain imaging data of each subject to a shared representation space applicable to a large-scale group, and finally achieving accurate brain function alignment between subjects. According to the method, graph representation information generated in the shared representation space can also be used for accurately predicting the brain function state and behavioral index of the subjects.

公开(公告)号：US20250125003A1

公开(公告)日：2025-04-17

申请号：US18608945

申请日：2024-03-19

Applicant: ZHEJIANG LAB

Inventor： Zenghui XU ,  Jin TANG ,  Yu ZHANG ,  Gaoxiang CHEN ,  Ting YU ,  Jin ZHAO ,  Ji ZHANG

IPC: G16B15/00

Abstract: A graph calculation method of RNA similarity analysis, an apparatus, a device, and a medium are provided. The method includes: converting sequence data of a looked-up RNA into a looked-up RNA structure graph; obtain a first similarity between the looked-up RNA structure graph and a target RNA structure graph; obtaining a second similarity based on the number of base constituent structures in the looked-up RNA structure graph and the number of base constituent structures in the target RNA structure graph; reconstructing the looked-up RNA structure graph based on the base constituent structures in the looked-up RNA structure graph to generate a looked-up RNA higher-order graph; and analyzing similarity between the looked-up RNA higher-order graph and a target RNA higher-order graph to obtain a third similarity; and obtaining a final similarity between the looked-up RNA and the target RNA based on the first similarity, the second similarity, and the third similarity.

公开(公告)号：US20250013615A1

公开(公告)日：2025-01-09

申请号：US18595474

申请日：2024-03-05

Applicant: ZHEJIANG LAB

Inventor： Junyang ZHANG ,  Ruonan ZHENG ,  Zhaoxiang WANG ,  Zhichao WANG ,  Chen WANG ,  Yu ZHANG ,  Tianzi JIANG

IPC: G06F16/21

Abstract: An information recommendation method, an apparatus, a device, and a medium based on embedding table compression are provided. The method includes: determining, based on a preset compression ratio, to-be-compressed features and non-compressed features in a to-be-compressed embedding table of a recommendation model, generating a similarity index matrix based on a similarity between the to-be-compressed features and the uncompressed features; generating an index dictionary based on the similarity index matrix; substituting a first feature mapping dictionary based on the index dictionary to generate a second feature mapping dictionary, wherein the first feature mapping dictionary is generated based on a data set; and acquiring to-be-recommended data, replacing features in the to-be-recommended data according to the second feature mapping dictionary, inputting replaced features into the recommendation model, and outputting a prediction result.

公开(公告)号：US20240389880A1

公开(公告)日：2024-11-28

申请号：US18796264

申请日：2024-08-06

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Chaoliang SUN ,  Zhichao WANG ,  Huan ZHANG ,  Haotian QIAN ,  Tianzi JIANG

IPC: A61B5/055 ,  A61B5/00 ,  G06T7/00

Abstract: A system for precisely locating abnormal areas of brain fiber bundles. The system extracts fiber connections of the whole brain from diffusion magnetic resonance data, and fiber bundle pathways extracts through self-defined fiber bundle pathways or based on brain fiber bundle templates. A selected fiber bundle pathway is projected on a fiber connection result of the whole brain and finely segmented. The imaging indexes such as fractional anisotropy, mean diffusivity, intra-neurite volume fraction and orientation dispersion index are calculated from diffusion magnetic resonance data, so as to obtain the imaging index of each node of each fiber bundle pathway. These imaging indexes are configured to classify the disease group and the healthy group by a machine learning method, and which nodes on which fiber bundle pathways have abnormal changes with different diseases can be precisely located.

公开(公告)号：US20240393417A1

公开(公告)日：2024-11-28

申请号：US18796233

申请日：2024-08-06

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Zhichao WANG ,  Chaoliang SUN ,  Huan ZHANG ,  Haotian QIAN ,  Junyang ZHANG ,  Tianzi JIANG

IPC: G01R33/561 ,  G01R33/56 ,  G01R33/565

Abstract: A method and a system for simulating magnetic resonance echo-planar imaging artifacts. Firstly, for K-space artifacts, K-space data are restored through normal magnetic resonance images, and the K-space data are modified pertinently, and then images with artifacts are reconstructed; for susceptibility artifacts, a susceptibility model is constructed through normal magnetic resonance images, and the magnetic field distribution is reconstructed, and then the images with distortion artifacts are reconstructed. According to the present disclosure, a large number of artifact data sets with different artifact types and artifact degrees can be quickly created through a small number of normal images, thus laying a foundation for the research of identifying artifacts, eliminating or weakening artifacts. A simulation algorithm is designed according to the principle of generation of EPI sequence artifacts, and the obtained images such as stripe artifacts, Moer artifacts, Nyquist artifacts, susceptibility artifacts and the like have good scientificity, accuracy and interpretability.

公开(公告)号：US20240386244A1

公开(公告)日：2024-11-21

申请号：US18427844

申请日：2024-01-31

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Huan ZHANG ,  Jing ZHANG ,  Yuanyuan LI ,  Zhichao WANG ,  Tianzi JIANG

IPC: G06N3/0455 ,  A61B5/055 ,  G01R33/48 ,  G01R33/56 ,  G06N3/08

Abstract: A cognitive training material generation method, a cognitive training method, a device, and a medium are provided. The cognitive training material generation method includes: acquiring a first feature and a second feature, the first feature including a multimedia material and semantic information corresponding to the multimedia material, the second feature including a magnetic resonance representation; fitting the first feature and the second feature, obtaining a semantic map according to a fitting result and a preset brain map, and acquiring target semantic information corresponding to a target point according to the semantic map; taking the first feature as input of a deep learning model and the second feature as a constraint of the deep learning model, training the deep learning model, and determining a weight parameter of the deep learning model; generating a cognitive training material according to the target semantic information and the weight parameter of the deep learning model.

公开(公告)号：US20240398305A1

公开(公告)日：2024-12-05

申请号：US18798861

申请日：2024-08-09

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Haotian QIAN ,  Chaoliang SUN ,  Zhichao WANG ,  Huan ZHANG ,  Tianzi JIANG

IPC: A61B5/245 ,  A61B5/00

Abstract: A system for classifying working memory task magnetoencephalography based on machine learning, including: the magnetoencephalography data acquisition module configured to acquire magnetoencephalography data of a subject in different working memory task states; the magnetoencephalography data preprocessing module configured to control the quality of magnetoencephalography data in different working memory tasks and separate noises and artifacts; the magnetoencephalography source reconstruction module configured for sensor signal analysis and source reconstruction analysis for the data processed by the magnetoencephalography data preprocessing module; and the machine learning classification module is configured to classify the working memory tasks to which the subjects belong by taking power time series as features. The present disclosure integrates the complete analysis pipeline from preprocessing to source reconstruction of the working memory magnetoencephalography data, classifies the working memory task magnetoencephalography data, and is of great significance to the study of working memory decoding and brain memory related mechanisms.

公开(公告)号：US20240303277A1

公开(公告)日：2024-09-12

申请号：US18396493

申请日：2023-12-26

Applicant: ZHEJIANG LAB ,  Huazhong University of Science and Technology

Inventor： Yu ZHANG ,  Hao QI ,  Kang LUO ,  Jin ZHAO ,  Zhan ZHANG

IPC: G06F16/901

CPC classification number: G06F16/9024

Abstract: Systems, methods, devices and storage media for graph data processing are provided. In one aspect, a graph data processing system includes a memory and a plurality of processing units, and each processing unit is provided with a decision module. Each processing unit is configured to determine set operations required for extracting one or more subgraphs matching a specified graph pattern from target graph data according to a preset graph pattern matching algorithm. Then, for each set operation, the decision module is configured to determine a cost value corresponding to a performance of the processing unit occupied to execute the set operation in accordance with different execution policies, and further select a target execution policy with a smallest cost value to execute the set operation.