58 results about "Cell feature" patented technology

The invention relates to the technical field of model analysis, in particular to an imaging flow cytometry cell detection method based on an improved model. The method comprises the following steps: introducing a cell sample to be detected into an imaging flow cytometry system integrated with a micro-fluidic chip for continuous image acquisition to generate an initial cell image sequence; an automatic digital focusing algorithm is applied to the initial cell image sequence, and a cell image frame set with the optimal focal plane is screened out; inputting the cell image frame set into a preset PA-YOLO improved model for multi-dimensional extraction and fusion, and generating a multi-scale cell characteristic spectrum; carrying out refined feature learning and cell target positioning and classification on the multi-scale cell feature spectrum, and outputting a cell detection result; and carrying out validity verification on the cell detection result, and carrying out comparative analysis in combination with an imaging flow cytometry system to generate a cell detection report. According to the method, the imaging quality and the detection accuracy of cell images with different depths can be remarkably improved.

The invention discloses a fault diagnosis method for a series energy storage lithium battery pack under a charging condition, and the method comprises the following steps: (1), monitoring and collecting the single voltage and temperature of each cell in real time, and uploading the single voltage and temperature; (2) taking the single voltage of the battery cell as the characteristic of the battery cell and expanding; (3) expanding the characteristics of the battery cells, and introducing a voltage correlation coefficient between the battery cells as a new characteristic dimension; (4) calculating a correlation coefficient change rate of the current time window and the previous time window as a new feature; (5) calculating a Z-Score score of the information entropy as an expansion feature; (6) determining the characteristic vectors of the single cells to obtain a final characteristic matrix; (7) the feature matrix is put into an isolated forest algorithm for abnormal point identification; and (8) further analyzing and screening the battery cell data points marked as abnormal, and determining an abnormal reason and a fault type. The method does not need battery modeling, is suitable for various different types of battery cells, and is simple, convenient and rapid.

The invention relates to the technical field of medical image processing, and discloses a tumor cell accurate recognition and analysis system based on a digital pathological image, which effectively overcomes the problem of global context deficiency caused by traditional pathological image blocking processing by constructing a microcosmic and macroscopic parallel multi-scale feature extraction mechanism. A cell topological graph is constructed by utilizing spatial semantic double constraints to simulate a biological spatial distribution rule of tumor cells, and precise navigation and weighted enhancement of microscopic cell characteristics by macroscopic organization structure information are realized through a cross-scale attention aggregation technology. Therefore, the model can fully refer to the surrounding microenvironment when identifying the heterotypic cells, and the misjudgment risk caused by background noise or local form similarity is remarkably reduced; in addition, a structured decision-making mechanism based on manifold consistency eliminates isolated prediction noisy points and ensures the continuity and rationality of a diagnosis result on a biological structure.

The application relates to an integrated circuit technology field, and discloses a delay prediction method and a computer readable storage medium. The method comprises the following steps: obtaining a to-be-calibrated circuit, and converting the to-be-calibrated circuit into graph structure data; calculating the to-be-calibrated circuit by using a timing analysis tool to determine a to-be-calibrated delay of the to-be-calibrated circuit; encoding the graph structure data corresponding to the to-be-calibrated circuit to determine a cell feature vector, a node feature matrix and an edge feature matrix; fusing the node feature matrix and the edge feature matrix by using a feature extraction model to obtain a fused node feature matrix; aggregating the fused node feature matrix to obtain a graph-level feature vector; integrating the cell feature vector and the graph-level feature vector to determine a context feature vector; splicing the context feature vector and the to-be-calibrated delay to determine a combined feature vector; performing residual prediction on the combined feature vector, and correcting the to-be-calibrated delay based on the predicted residual to obtain a calibrated delay.

The application relates to a kind of mitochondria-based single cell feature extraction and analysis method and system, comprising: obtaining the multiple modal images such as bright field image, nucleus fluorescent image and mitochondria fluorescent image of single cell;Image preprocessing is carried out to the three modal images obtained;For different structures such as mitochondria, morphological and texture features are extracted, and feature analysis is carried out;Further, through the fusion of mitochondria and machine learning technology, the automatic classification of cells is realized.The application is used for the classification of human cervical epithelial cells (H8) and cervical cancer cells (HeLa), and the machine learning analysis of morphological features and texture features shows the potential of mitochondria in the classification of cervical cells.The application has strong applicability, can be combined with machine learning and other analysis methods, and can be applied to various biological cells, has universality, and is easy to popularize.

Methods, systems, and devices for wireless communication are described that provide for cell selection or reselection at a user equipment (UE) based on cell selection preference criteria of the UE. The UE may receive a set of cell selection criteria that provides a priority order for cell selection based on one or more cell features, cell types, or combinations thereof. The UE, based on one or more signal measurements of available cells and the cell selection criteria, may select one of the available cells for communications. The UE may also maintain a feature cell database in which a number of cells and an associated feature mask may be stored and used to identify cells having features or types associated with the cell selection criteria for use in cell prioritization. Cell selection criteria may be used for cell selection / reselection procedures, mobility procedures, or any combinations thereof.

The invention relates to the technical field of image recognition, and particularly discloses an auxiliary blood tumor pathological diagnosis system and method based on artificial intelligence, and the system extracts a pathological image group of a patient through a pathological diagnosis auxiliary platform, analyzes the data of each pathological image, and judges the effective feature value of each pathological image; the effective feature values of the pathological images are compared with a predefined effective feature threshold value to obtain a comparison result, and the pathological diagnosis auxiliary platform judges whether the effective features of the pathological images are enhanced or not based on the comparison result; and extracting cell characteristic data in each pathological image according to each pathological image, judging the characteristic complexity of each pathological image, and performing difference analysis on the pathological image group to complete auxiliary blood tumor pathological diagnosis.

The invention provides a reverse design method of an energy absorption lattice structure based on a genetic algorithm, and belongs to the field of energy absorption lattice structure optimizing.The method comprises the steps that the angle range and the distance range of a unit cell are determined; according to the angle range and the distance range, the angle parameters and the distance parameters are combined, and a unit cell set composed of a plurality of unit cells is obtained; simulating each unit cell in the unit cell set to obtain unit cell feature data; updating the unit cell set according to the unit cell feature data corresponding to each unit cell; determining target feature data according to the updated unit cell set; and according to the target feature data, constructing an energy absorption lattice structure. According to the scheme provided by the invention, the quasi-static compression finite element simulation is carried out to obtain the stress-strain curve of the energy-absorbing lattice structure, and the angle and distance of the unit cells in the energy-absorbing lattice structure are optimized based on the genetic algorithm by taking a larger platform stress value and densified strain as optimization targets; the structural energy absorption capacity of the energy absorption lattice structure can be improved.

An example method for characterizing and evaluating cells within images includes identifying, using machine-learning logic executed on a processor that is trained using cell image training data including digital microscopy images labeled with cell features, one or more cells in an image from a digital microscopy system, dividing an area of the image including the one or more cells into distinct regions to generate measurements of pixel value changes between the distinct regions, determining a presence and a location of RNA within the one or more cells in the image based on the measurements of pixel value changes, and categorizing, using the machine-learning logic executed on the processor, the one or more cells in the image when the presence and the location of the RNA is determined within the image of the one or more cells.

Example systems, methods, and non-transitory computer readable media are directed to obtaining a point cloud that represents an environment based at least in part on a plurality of points in three-dimensional space; determining corresponding classifications of points in the point cloud as ground or not-ground based at least in part on a plurality of ground classification algorithms; determining respective point cloud features associated with the points in the point cloud; determining respective cell features associated with a plurality of cells that segment the point cloud; generating feature data for a machine learning model based at least in part on one or more of: the classifications of the points based on the plurality of ground classification algorithms, the point cloud features, or the cell features; and classifying the points in the point cloud based at least in part on an output from the machine learning model.

This invention discloses a cross-modal cell alignment method for pathological images in non-registerable scenarios. The method acquires H&E and mIF stained images and their data-augmented views corresponding to spatial anatomical locations; constructs H&E modality processing branches, mIF modality processing branches, and a reverse self-attention module; and sequentially performs three-stage progressive training: intra-modal contrastive pre-training based on the data-augmented view; using mIF modality-level semantic aggregation tokens from the same spatial anatomical location as positive samples and mIF modality-level semantic aggregation tokens from different spatial anatomical locations as negative samples, achieving H&E to mIF modality-level semantic alignment through contrastive learning; inputting the aligned H&E modality-level semantic aggregation tokens and the projected H&E cell feature embedding set into the reverse self-attention module, and achieving unsupervised cell-level semantic alignment by minimizing the Hungarian matching loss; finally, the output H&E cell features semantically aligned with mIF are embedded into a refined set, which can be directly used for downstream pathological analysis tasks such as high-precision cell clustering.

The present application relates to a kind of pathological image cell population feature extraction method and system, wherein the method comprises the following steps: obtaining pathological image;Pathological image is input into cell nucleus segmentation model, and cell nucleus segmentation result is obtained;Pathological image is cropped based on cell nucleus segmentation result, and the cell image of each cell in pathological image is obtained;Cell image is input into the cell feature extraction model based on contrast learning, and the feature and cell location information of each cell are obtained;The feature set of all cells and corresponding cell location information are input into the population feature extraction model based on contrast learning, and the cell population feature is obtained, wherein, population feature extraction model generates positive sample of contrast learning by randomly selecting rectangular region of different size, arbitrary rotation angle in pathological image during training process.Compared with prior art, the present application has the advantages of good robustness, strong generalization ability and strong explainability.

The single cell identification described herein utilizes cell image information and extracts cell features with a neural network model to subtly distinguish the noise events from single cells, allowing the user to choose which different types of noise events to exclude depending on the requirement of applications. The fast neural network model is able to extract more abundant and specific cell features than handpicked features, which enables the model to be equipped with higher accuracy and higher discriminative capability of distinguishing noise events and identifying the single cells in real-time. Utilization of a neural network model for real-time single cell identification represents a novel technique never applied before. It allows high discriminative capability and high accuracy compared to traditional FACS (Fluorescence-activated Cell Sorting). The usefulness of this technique is to integrate with any brightfield (BF) model and fluorescence (FL) model to identify single cells for different downstream applications.

The systems and methods described herein relate to performing one or more highly multiplexed cell assays. In some embodiments, one or more channels of a fluidic device are provided with a photopolymerizable polymer precursor and cells randomly arranged on a surface, followed by measurement of cell position by a detector, synthesis of a hydrogel chamber via photopolymerization to encapsulate individual cells, and loading of assay reagents into the channels. An assay signal indicating the desired cellular characteristics is generated for each of the encapsulated cells.

The present application relates to a single-cell protein subcellular localization model based on weakly supervised multi-instance learning, and relates to the technical field of biological information. The model comprises a cell feature extractor, an image branch, a cell branch and a class-aware adaptive pruning module. The cell feature extractor is used to encode single-cell images into single-cell feature vectors. An image-level classifier in the image branch is used to output an image-level protein subcellular localization prediction result according to the image-level feature representation. A cell-level classifier in the cell branch is used to output a single-cell-level protein subcellular localization prediction result according to the single-cell feature vector. The class-aware adaptive pruning module generates single-cell pseudo labels through several label sources. The model of the present application can solve the problems of cell label noise and class long-tail distribution under weak supervision, thereby realizing more accurate and stable single-cell localization prediction and providing a powerful tool for single-cell heterogeneity analysis.

The invention relates to a method for determining user features and classifying cancer users, a medium and equipment, and relates to the technical field of machine learning. The method comprises the steps of calculating feature importance degrees of original cell features of a target user based on a preset weight value calculation model, and sorting the original cell features based on the feature importance degrees to obtain first target cell features; constructing a first original feature map of the target user according to the first target cell feature, and inputting the first original feature map into a preset feature map processing model to obtain a first target feature vector; performing clustering processing on the target user based on the first target feature vector to obtain a first user clustering result, and determining a first contour coefficient according to the first user clustering result; and determining a target contour coefficient according to the first contour coefficient, and determining a target user feature corresponding to the target user according to the target contour coefficient. The calculation efficiency is improved.

The present application provides methods of evaluating a candidate agent for its safety for extraembryonic development or embryonic development, comprising: i) contacting an early extraembryonic cell and / or a cell differentiated therefrom with a test sample comprising the candidate agent and / or a metabolic product thereof; and ii) assessing change ofone or more cell features of the early extraembryonic cell and / or the cell differentiated therefrom relative to a reference early extraembryonic cell and / or a cell differentiated therefrom without contacting with the test sample. Compositions useful for such methods are also provided.

A system is disclosed that enables the automated measurement of cellular mechanical parameters at high throughputs. The microfluidic device uses intersecting flows to create an extensional flow region where the cells undergo controlled stretching. Cells are focused into streamlines prior to entering the extensional flow region. In the extensional region, each cell's deformation is measured with an imaging device. Automated image analysis extracts a range of independent biomechanical parameters from the images. These may include cell size, deformability, and circularity. The single cell data that is obtained may then be used to in a variety of ways. Scatter density plots of deformability and circularity may be developed and displayed for the user. Mechanical parameters such as deformability and circularity may be gated or thresholded to identify certain cells of interest or sub-populations of interest. Similarly, the mechanical data obtained using the device may be used as cell signatures.

The invention provides a cell sorting method based on light-induced dielectrophoresis virtual channel and dynamic path planning, which comprises the following steps: acquiring an original microscopic image of a cell in real time by using an optical microscope, and acquiring the size and real-time position of the cell through image preprocessing and cell feature extraction operation; determining a respective corresponding target sorting area based on the cell size and the cell position of each cell; on the basis of the current cell position, global path planning is carried out by using a time-space heuristic search algorithm with introduced time dimension, and a global path point sequence containing time sequence information is obtained; projecting the path plan of each cell to a light-induced dielectrophoresis chip by using a digital projector to form a virtual channel so as to guide each cell to move to a respective corresponding target sorting area; and establishing a dynamic potential field environment by utilizing a multi-potential field fusion algorithm so as to adjust parameters of the operation light spots, so that the actual motion trail of the cells tends to the virtual channel. The method is high in anti-interference capability and high in sorting result accuracy.

The present disclosure provides a method of classifying a tissue sample by a classification system, the method comprising: identifying, by the classification system, a plurality of tiles corresponding to full slice image data of the tissue sample; generating, by the classification system, a plurality of semantic masks corresponding to the plurality of tiles, each of the plurality of semantic masks identifying a cell boundary and a cell type of each cell within a corresponding tile of the plurality of tiles; generating, by the classification system, a plurality of cellular features for each tile of the plurality of tiles based on a corresponding semantic mask of the plurality of semantic masks; and classifying, by the classification system, the tissue sample based on the plurality of cell characteristics for each tile of the plurality of tiles.

This invention relates to the field of cell image analysis and biological cell screening technology, and discloses a method for screening liver stem cells. The method includes: acquiring original images; performing effective image screening; performing background correction, noise reduction and enhancement, and contrast compensation; performing single-cell instance segmentation; performing single-cell feature extraction and trajectory association; determining target cells using a neural network model; and outputting the liver stem cell determination result. Compared to existing screening methods that rely on manual observation or single-frame static image discrimination, especially in the case of primary hepatocyte suspensions containing mature hepatocytes, bile duct epithelial cells, stromal cells, and cell debris, this method addresses the technical problem of unstable and automated identification of liver stem cells. This application improves the accuracy and automation of liver stem cell screening by constructing a continuous processing flow of image quality gating, instance segmentation, and dual-branch feature fusion judgment.

The present disclosure relates to processing cell images in a convolutional neural network with contextual data. The present disclosure relates to image processing in machine learning convolutional neural networks. The technology can improve the accuracy and reliability of applications using machine learning convolutional neural networks, particularly in the field of cell microscopy. Contextual data is provided which indicates cell features determined globally for a scene, and this data is then taken into account in the inference of the machine learning convolutional neural network.

This invention discloses a method and system for identifying abnormal stem cell features based on multimodal data. It involves intelligent analysis of bioinformatics data and cell feature identification. The method collects multimodal source data of stem cells and performs standardized preprocessing and batch effect correction. It constructs a cross-modal feature encoding network embedded with a prior biological knowledge graph of stem cells to generate fused features. It builds a baseline feature library of normal stem cells to perform initial screening of abnormal features. It identifies core abnormal features through temporal dynamic correlation analysis, verifies the correlation of abnormal phenotypes through causal inference, and stores the identification results in a knowledge base while simultaneously performing incremental iterative optimization of the model. This invention improves the comprehensiveness of stem cell abnormal feature identification by relying on multimodal data fusion and biological prior constraints, and enhances identification accuracy and interpretability by combining temporal analysis and causal verification. The model supports continuous iteration, providing stable and efficient technical support for stem cell quality control and abnormal mechanism analysis.

The application provides a pathological whole section image feature extraction large model construction method and application based on hyperbolic space, including the following steps: constructing a pathological whole section image feature extraction architecture and a text constraint module, obtaining multiple training sample pairs to form a training sample set; training the pathological whole section image feature extraction architecture in a batch training manner based on the training sample set, and obtaining a total loss function in the training process; and adjusting parameters of the pathological whole section image feature extraction architecture based on the total loss function to obtain a pathological whole section image feature extraction large model. The scheme comprehensively captures fine-grained cell features and local tissue context information by extracting block-level initial features and region-level initial features of pathological whole section images and associating spatial coordinates, and retains the whole section topological structure to provide a rich multi-scale feature basis for coding.

The application discloses a series energy storage lithium battery pack fault diagnosis method under a charging condition, comprising the following steps: (1) real-time monitoring and collecting single cell voltages and temperatures of each cell, and uploading; (2) taking the single cell voltage of the cell as a cell feature and expanding the same; (3) expanding the cell feature, and introducing a voltage correlation coefficient between the cells as a new feature dimension; (4) calculating a correlation coefficient change rate of a current time window and a previous time window as a new feature; (5) calculating a Z-Score score of information entropy as an expanded feature; (6) determining a cell single feature vector, and obtaining a final feature matrix; (7) putting the feature matrix into an isolated forest algorithm for abnormal point identification; (8) further analyzing and screening cell data points marked as abnormal, and determining an abnormal reason and a fault type. The method does not need to model the battery, is suitable for various different types of cells, and is simple, fast and convenient.

The invention provides a pseudo-tag-guided single cell feature space optimization method, which comprises the following steps: acquiring a data set, and preprocessing the data set; constructing a feature extraction network model, and mapping the preprocessed data set to a unified embedding space to obtain an initialized embedding representation; inputting the samples into a classifier, generating a pseudo tag, taking the maximum value as a confidence score, comparing the confidence score with a preset confidence threshold, and dividing the confidence score into a high-confidence sample set and a low-confidence sample set; constructing a feature space structure optimization model, and designing a structured loss function; s5, minimizing the structured loss function, updating parameters of the feature extraction network model, and repeating the steps S3-S5 until the model converges; the target modal cells are predicted by using the convergent feature extraction network model and the classifier, so that category confusion caused by excessive alignment is effectively prevented through double structure constraints, and the accuracy of label migration, the F1 score and the performance of open set recognition are remarkably improved.

The present application relates to the technical field of energy storage system, and discloses a container energy storage system health state prediction method, device and program product, comprising: performing physical topology modeling on the container energy storage system to obtain a multi-dimensional weight adjacency matrix; based on the multi-dimensional weight adjacency matrix and historical temperature data set of the container energy storage system, performing multi-objective optimization by using an improved non-dominated sorting genetic algorithm, and determining master cell groups and slave cell groups of the container energy storage system; according to the master cell groups and the slave cell groups, obtaining original signals of the container energy storage system; based on the original signals, processing by an anti-aliasing filtering method and a signal preprocessing method, and establishing a target master cell feature matrix; based on the target master cell feature matrix and the multi-dimensional weight adjacency matrix, performing space-time graph neural network prediction to obtain slave cell health state prediction results of the container energy storage system, improve the prediction error stability and accuracy, and reduce the cost.

A cell annotation method and apparatus, an electronic device, and a computer program product. The method comprises: inputting a gene expression matrix of a target cell into a hierarchical cell annotation model, and extracting M levels of cell features; and on the basis of the cell features, outputting an annotation result of the target cell in a hierarchical cell classification system, wherein the cell features comprise global features and local features, the global features are used for determining the level to which the cell belongs and a global cell type, and the local features are used for determining local cell classification of each level.

The application discloses a kind of classification detection systems based on cell fluorescence image, including: image acquisition module, image processing module and type labeling module;First, image acquisition module generates four kinds of fluorescence characteristic images and Merge characteristic images with four kinds of fluorescence characteristics through orthosteric microscope;Second, image processing module is preprocessed, positioning, cell cluster segmentation, cell feature extraction and classification to fluorescence image;Finally, image type labeling module labels the type of cell area image classified by image processing module, obtains the cell area image with annotation.The application carries out classification detection to the fluorescence image collected, can accurately locate cell position and effectively identify different cell types, can accurately segment for the adherent cell existing in image, and also can effectively identify for the boundary cell in field of view, fluorescence brightness too dark cell.

The embodiment of the invention provides user equipment and an execution method thereof, and relates to the field of artificial intelligence. The method comprises: predicting a first wireless channel state component related to a radio access technology (RAT) change and a second wireless channel state component related to a cell feature; and obtaining a predicted wireless channel state based on the first wireless channel state component and the second wireless channel state component. Optionally, the method performed by the electronic device may be performed using an artificial intelligence model.