5623results about "Acquiring/recognising microscopic objects" patented technology

The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.

A method of self-testing an imaging system of a sample handling apparatus is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

A method of self-testing an imaging system of a sample handling apparatus is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

Methods, apparatus and program products for acquiring and analyzing images of a sample. An artificial intelligence (AI) module may be trained to identify, within multi-dimensional image data corresponding to images of objects, wavelength patterns corresponding to one or more defects within the objects. An analysis module is configured to detect one or more defects in the sample using the AI module for analyzing the images of the sample. In some embodiments, the samples are food samples and the defects include one or more pathogens.

The invention discloses a traditional Chinese medicine identification and analysis system based on clustering analysis, and relates to the technical field of medicine analysis and identification, and the system comprises the following modules: a data acquisition module, which is used for obtaining traditional Chinese medicine multi-modal data and processing asynchronous stream data, the multi-modal data comprises microscopic images, spectral data, metabonomics data and electronic nose / tongue sensing data, and the step of processing the asynchronous stream data comprises timestamp marking, multi-device data calibration and feature caching. According to the traditional Chinese medicine identification and analysis system based on clustering analysis, through collaborative design of a multi-modal hypergraph space-time encoder and a drug property causal intervention mechanism, multi-dimensional and multi-level leap-type improvement of a traditional Chinese medicine identification technology is realized, and the core contradiction of disjunction of a traditional clustering algorithm and domain knowledge is solved; and a universal framework of cross-modal dynamic learning is provided for the field of medical informatics.

The invention relates to the technical field of medical image processing, and provides a medical image segmentation method based on wavelet enhancement and multi-scale feature fusion. According to the method, a CNN-Transform double-branch coding structure is combined, a multi-scale wavelet fusion module is provided, from the perspective of a frequency domain, Haar wavelet transform is adopted to extract an image high-frequency sub-band so as to enhance edge and texture detail expression, dynamic weighting is performed on different frequency band features through grouping convolution and a sub-band attention mechanism, and the discrimination capability is improved; meanwhile, a multi-scale cavity pyramid structure is fused in a spatial domain, and after cross attention dynamic fusion is introduced, a feature alignment mechanism of a wavelet domain and the spatial domain is established; and collaborative fusion of frequency domain and space domain features is realized. The method effectively improves the segmentation precision of the fuzzy boundary and the fine-grained structure under the complex background, has good universality and adaptability, and is suitable for various medical image segmentation tasks.

The invention relates to the technical field of cell culture data processing, in particular to a cell culture information data management method and system. The method comprises the following steps: performing culture environment multi-parameter high-frequency real-time monitoring on a cell culture environment box to obtain multi-dimensional cell culture environment time sequence parameters; performing culture environment key disturbance parameter screening on the multi-dimensional cell culture environment time sequence parameters to generate environment key disturbance parameters; acquiring historical cell culture batch data; based on historical cell culture batch data, carrying out multi-parameter coordinated regulation and control on the environment key disturbance parameters to obtain a cell culture environment regulation and control strategy; regulating and controlling the cell culture process according to the cell culture environment regulation and control strategy, and performing cell culture knowledge graph construction on historical cell culture batch data to obtain a cell culture management knowledge graph. According to the invention, intelligent management of cell culture data is realized through construction of the cell culture knowledge graph.

Methods and systems utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analysis framework that identifies and quantifies biological properties / analytes from imaging data, and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach using imaging to examine underlying biology as an intermediate for assessing pathology provides numerous analysis and processing advantages over systems and methods configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

The invention provides a circulating tumor cell recognition model training method and system based on deep learning, and the method comprises the steps: obtaining a multi-channel pathological sample image set of circulating tumor cells, carrying out the cross-modal feature extraction of a multi-channel pathological sample image, generating a fusion feature vector containing the morphological features and microenvironment features of the cells, and carrying out the recognition of the circulating tumor cells through the fusion feature vector. A deep convolutional network with a multi-stage training strategy is constructed, network parameters of the deep convolutional network in the training process are verified and adjusted by adopting a hierarchical cross validation mechanism, and finally a final recognition model is generated based on the verified and adjusted network parameters. The final recognition model is configured to receive the clinical pathology image stream and output localization coordinates and classification confidence of circulating tumor cells. According to the method, a circulating tumor cell recognition result can conform to a cell space distribution rule and has clinical pathological diagnosis interpretability, and the problems of high false positive rate and insufficient cell subtype discrimination caused by neglecting of microenvironment interaction in a traditional method are solved.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A method based on a multi-task learning model comprises the following steps: acquiring and processing histopathological image data of gastritis through a data preparation and preprocessing step; feature extraction is performed by using a self-supervised learning pre-trained model, and image blocks are coded into high-dimensional feature vectors; and constructing a multi-task learning model, learning feature representation through a full connection layer module and an attention layer module, and outputting a classification result of each task. And carrying out model training and optimization by using an optimizer, adding multitask loss through a loss function, and dynamically adjusting the model performance. The trained model can automatically detect and grade gastritis, atrophy, acute activity, intestinal metaplasia and other pathological indexes, outputs a standardized evaluation result, and assists in pathological diagnosis. According to the method, a multi-task deep learning framework based on self-supervised learning pre-training is constructed, a traditional single-task modeling mode is broken through, deep learning framework design is driven through pathological index association, and accuracy and clinical practicability of non-neoplastic disease assessment of the stomach are remarkably improved.

Computational refocusing-assisted deep learning methods, apparatus, and systems are described. In certain pathology examples, a representative image is generated using a machine learning model trained with uniformly focused training images generated by a Fourier ptychographic digital refocusing procedure and abnormalities are automatedly identified and / or enumerated based on the representative image.

The present invention relates to the technical field of oil and gas exploration and development. Provided are a digital core-based shale pore type recognition method and a quantitative characterization method. The digital core-based shale pore type recognition method comprises: on the basis of a gray image of a shale sample to be recognized, recognizing an organic matter and a transition zone to obtain an organic matter recognition result; and on the basis of the organic matter recognition result and the gray image, obtaining an organic pore image and an inorganic pore image as a shale pore type recognition result. The present invention can be used to quantitatively recognize organic pores, inorganic pores and micro-cracks of shale, quantitatively characterize shale pore structures, and analyze structural parameters of the organic pores, the inorganic pores and the micro-cracks, so as to perform statistical analysis of distribution of different types of reservoir spaces of shale, thereby being of great significance for shale reservoir quality evaluation and effective shale oil and gas development method optimization.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and / or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

The invention discloses an IG-MambaUNet image segmentation model, a model training method and an application method thereof, the IG-MambaUNet image segmentation model comprises an encoder and a decoder which are symmetrically arranged in parallel, the encoder comprises three first encoding modules and a second encoding module, the three first encoding modules are sequentially connected from top to bottom, the second encoding module is connected with the first encoding module on the bottommost layer, and the third encoding module is connected with the second encoding module on the bottommost layer. The input end of the first coding module on the topmost layer is connected with a first convolution and maximum pooling operation module, the first coding module is formed by coupling an IG-Mama module and a Patch Merging module, the second coding module comprises an IG-Mama module, the IG-Mama module comprises a local feature extraction module, a global feature extraction module and a gating attention module, and the global feature extraction module comprises a local feature extraction module, a global feature extraction module and a gating attention module. The decoder comprises a second coding module and a first coding module which are sequentially connected from bottom to top, and the output end of the first decoding module on the topmost layer is connected with a second convolution and maximum pooling operation module. According to the scheme, fine segmentation is realized, and the method can be suitable for automatic cell body segmentation of histological dyed ganglion images with complex textures and fine structures.

An optical biomodule for detecting a disease specific biomarker(s) at an early stage, utilizing enhanced fluorescence emission (due to integration of three-dimensional (3-D) structures in a fluidic container) upon chemical binding / coupling of a disease specific biomarker(s) with its corresponding disease specific biomarker binder(s)-coupled with a fluorophore(s) positioned horizontally within a geometrical area (that includes a center region or a middle region) is disclosed. Furthermore, a corresponding disease specific biomarker binder(s) may include an amplifying chemical structure / sequence. The three-dimensional (3-D) structure(s) may be coupled with an optical resonator and / or a photonic crystal (PC) and / or a metamaterial and / or a metamaterial of Epsilon-Near-Zero (ENZ) of a suitable wavelength range.

The invention discloses an immunohistochemical digital pathological image processing method, and relates to the technical field of image processing, and the method comprises the steps: obtaining an immunohistochemical staining pathological image, and carrying out the preprocessing; carrying out region division on the preprocessed pathological image, carrying out feature extraction and fusion on each sub-region, and generating a multi-dimensional feature vector; based on the multi-dimensional feature vector, optimizing the pathological image by using a region growing and merging algorithm; carrying out dyeing intensity detection on the optimized pathological image, and classifying dyeing areas through a K-means clustering algorithm; and based on a classification result, performing quantitative analysis and generating an immunohistochemical image analysis report. According to the method, through preprocessing, feature extraction and fusion, and in combination with a region growing and merging algorithm, precise segmentation and classification analysis of the immunohistochemical staining region are realized, and the automation and recognition precision of image processing are improved.

The invention discloses an improved convolution neural network model for leukocyte calculation, and relates to the field of artificial intelligence and medical image processing. Aiming at the problems of inaccurate feature extraction, strong background noise interference and insufficient multi-scale feature fusion in a traditional leukocyte calculation method, the model realizes fine processing and feature enhancement of a medical image through a multi-level modular design; the image preprocessing module is used for completing white blood cell positioning and image adaptive division and enhancement; multi-scale edge, texture and gray features are fused by means of an image feature extraction module, and a composite feature vector is constructed; through multi-scale convolution, a double-layer attention mechanism and convolution layer calculation of the improved convolution neural network model module, precise focusing of white blood cell contours and cell nucleus features, background noise suppression and calculation efficiency optimization are realized; and finally, a calculation result is output through a visual terminal, so that the accuracy and robustness of leukocyte counting are remarkably improved, and efficient technical support is provided for medical microscopic image analysis.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and / or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

The invention discloses an alimentary canal pathological diagnosis visual language large model construction method based on reinforcement learning and application thereof, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, extracting pathological information through layout analysis and adaptive threshold processing, and recombining the pathological information into a structured data set containing an inference chain; secondly, a visual encoder and a multi-branch classifier are used for extracting features and confidence coefficients, and dynamic structured cue words are generated; and finally, inputting the image and the cue word into a multi-modal large model, carrying out supervised fine-tuning hot start, and carrying out reinforcement learning training by adopting a group relative strategy optimization algorithm in cooperation with a composite reward function containing format, semantics and diagnosis dimensions. The problems that a general model is prone to generating illusion in a pathological scene and lacks reasoning logic are solved, and the accuracy and logicality of pathological report generation are remarkably improved.

The invention discloses a microscopic hyperspectral image segmentation method based on multi-scale attention fusion, and aims to improve the precision and robustness of medical image segmentation. According to the method, advantages of Transform and CNN in extraction of global and local features are combined, an innovative fusion module MSA (multi-scale attention) is designed, and global features of the Transform are efficiently fused into local features of the CNN through multi-scale, channel attention and space attention mechanisms. Meanwhile, residual connection is introduced in the up-sampling stage of the Transform branch, so that the gradient disappearance problem is relieved, and the network expression capability is enhanced; an SE module is added behind each CNN module, and feature channel weights are dynamically adjusted, so that useful features are enhanced, and redundant information is suppressed. Experiments show that the method shows excellent segmentation precision, robustness and efficiency in a microscopic hyperspectral image segmentation task, and particularly shows significant advantages for challenges such as boundary blurring and tissue mixing of a cancerous region and image differences of different patients.

The invention provides a cell type and cell abundance identification method and system based on cross-modal training, relates to the field of image processing, and aims to solve the problems that the existing identification technology mostly depends on non-standard factors such as manual labeling position annotation information and the like, abundant morphological modes in a tissue pathological image are not fully utilized, and the identification accuracy is poor. And the identification reliability and accuracy are influenced. The method comprises the following steps: acquiring spatial transcriptomics data matched with pathological image-gene expression, and preprocessing the spatial transcriptomics data to obtain high-expression gene expression data, local image blocks, cell types and abundance tags; constructing a cross-modal joint representation learning model, and inputting high-expression gene expression data and local image blocks into the model for training; and predicting a to-be-predicted histological image based on the trained model. According to the method, the problems in the prior art are solved, the capability of predicting the cell abundance from the histological image is improved, and the spatial distribution of fine-grained cell types is fully revealed.

Provided herein are methods for image segmentation. An image having a plurality of pixels arranged in a plurality of dimensions is obtained. The image is provided to a pretrained machine learning model and a plurality of amplitudes for each of the plurality of pixel is received from the model. Each of the plurality of amplitudes for each of the plurality of pixels corresponds to exactly one of the plurality of dimensions. At least one instance in the image is identified by: determining a first basin associated with the at least one instance, where the basin consists of a contiguous subset of the plurality of pixels having an amplitude with a magnitude not exceeding first threshold, determining a second subset of the plurality of pixels having an amplitude with a magnitude exceeding the first threshold, where the second subset of pixels bounds the first subset of pixels, and constructing the at least one instance by a flow of the second subset of pixels to the first subset of pixels according to the amplitudes thereof.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

The invention provides a fine-grained phytoplankton microscopic image enhancement classification method and a model building method thereof, and belongs to the technical field of underwater image enhancement classification, and the method comprises the following steps: firstly, collecting phytoplankton samples from a plurality of sea areas, and obtaining image data through a microscopic imaging technology; screening, preprocessing and data enhancement are carried out on the images, and a high-quality training set and a high-quality test set are constructed; according to the method, a double-branch deep learning model HySwinFormer is developed, the double-branch deep learning model HySwinFormer comprises a multi-scale context capture branch based on hierarchical vision TransFormer and a local detail enhancement branch designed by adopting a parallel convolution kernel, and joint expression of features is realized through a designed weighted fusion strategy; and training and optimizing the model to obtain a final model. Experimental results show that the classification performance of the HySwinFormer model on multiple data sets is superior to that of an existing mainstream deep learning model, and the classification effect is remarkably improved.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention discloses an immune cell state analysis method and system, and relates to the technical field of image processing. The method comprises the following steps: acquiring a motion image sequence of immune cells by using microscopic imaging equipment; performing edge detection on the motion image sequence, and marking contour pixel data labels of immune cells; performing trajectory tracking on the immune cells under a time sequence, and outputting motion trajectory pixel data; inputting the motion trail pixel data into a dynamic change rate recognition model, obtaining a first-order change rate, a second-order change rate and a change rate mean value, and generating a dynamic change feature embedding vector; inputting into an immune cell state prediction large model for state prediction to obtain an active state prediction index, and performing active labeling on the immune cells according to the active state prediction index. The technical problems of inaccurate analysis and incomplete prediction of the immune cell activity state in the prior art are solved, and the technical effect of improving the accuracy and comprehensiveness of immune cell activity state prediction is achieved.

The invention discloses an intelligent processing method and system for multi-modal biological medicine data. The method comprises the following steps: acquiring a tumor tissue microscopic image and clinical characteristics of a diagnosed patient object; performing semantic coding on the clinical features to obtain clinical feature semantic understanding feature vectors; performing feature extraction on the tumor tissue microscopic image through a tumor tissue morphological feature extractor based on a deep neural network model to obtain a tumor tissue microscopic feature map; performing target significance analysis on the tumor tissue microscopic feature map to obtain a sequence of target manifestation tumor tissue microscopic local feature vectors; performing class attention interaction fusion processing on the sequence of the clinical feature semantic understanding feature vector and the target explicit tumor tissue microscopic local feature vector to obtain a multi-modal medical semantic fusion feature; and determining a tumor type label based on the multi-modal medical semantic fusion features. Therefore, the tumor type can be detected and judged, and powerful support is provided for the aspects of early diagnosis, personalized treatment and the like of tumors.