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Patent Agency Ranking
公开(公告)号：US12274544B2
公开(公告)日：2025-04-15
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
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.
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公开(公告)号：US12197330B2
公开(公告)日：2025-01-14
申请号：US18470346
申请日：2023-09-19
Inventor： Zhan Zhang , Yu Zhang , Jin Zhao , Haifei Wu
IPC: G06F12/0804
Abstract: The present disclosure provides a data storage system, including data cache module, data processing module, and a persistent memory. The data cache module includes an on-chip mapping data cache and an on-chip counter cache, where the mapping data cache is configured to cache mapping data, and when the free space of the mapping data cache is less than a preset threshold, the least recently used mapping data cache line will be evicted from the cache and written back to the persistent memory. The data processing module encrypts/decrypts persistent memory data by using their counters, and accesses the persistent memory blocks indicated by their corresponding mapping data. The persistent memory comprises the first and second storage regions for the latest checkpoint data and modified working data in the current checkpoint interval respectively.
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公开(公告)号：US12127826B2
公开(公告)日：2024-10-29
申请号：US18125640
申请日：2023-03-23
Applicant: ZHEJIANG LAB
Inventor： Yu Zhang , Wenyuan Qiu , Zhichao Wang , Chaoliang Sun , Haotian Qian , Jingsong Li
CPC classification number: A61B5/055 , G01R33/4806 , G06N3/042 , G06T7/0012 , G06T2207/10088 , G06T2207/20084 , G06T2207/30016
Abstract: The present disclosure discloses a brain atlas individualization method and system based on magnetic resonance and a twin graph neural network. Firstly, a feature is extracted from resting-state functional magnetic resonance imaging (rs-fMRI) by utilizing functional connectivity based on a region-of-interest, and at the same time, Fisher transformation and exponential transformation are performed on the feature; secondly, a corresponding adjacent matrix is extracted from T1-weighted magnetic resonance data in a data set; and then the twin graph neural network is designed for training and testing with the transformed feature and the adjacent matrix as inputs and a group atlas label and a sampling mask as outputs.
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公开(公告)号：US12118018B2
公开(公告)日：2024-10-15
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.
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公开(公告)号：US12223650B2
公开(公告)日：2025-02-11
申请号：US18796239
申请日：2024-08-06
Applicant: ZHEJIANG LAB
Inventor： Yu Zhang , Chaoliang Sun , Zhichao Wang , Huan Zhang , Haotian Qian , Tianzi Jiang
Abstract: A system for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging, which extracts the radiomics information of multiple brain regions across modals as the features of nodes from multimodal magnetic resonance data, and extracts the connectomics information between brain regions to form an adjacency matrix. T1-weighted structural images extract cortical information through cortical reconstruction, and resting-state magnetic resonance data are used to calculate amplitude of low frequency fluctuations, regional homogeneity and functional connectivity. Through multimodal data preprocessing, image index extraction and structured data integration, multimodal unstructured magnetic resonance image data are integrated into unified graph-structured data, and the disease is predicted by a graph convolutional neural network method. The system can better integrate the cross-modal physiological indexes of multiple brain regions and the correlation between brain regions and improve prediction ability of the model and generalization ability of the model with different diseases.
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