190 results about "Histopathology" patented technology

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

Systems and methods are provided for generating clusters of anomalous images. Histopathological samples are images at an associated imaging system to produce a plurality of images. A plurality of anomalous images are identified from the plurality of images at an anomaly detection system, having an associated latent space. The plurality of anomalous images are clustered to generate a plurality of clusters within a feature space defined by a set of classification features. The set of classification features include a feature derived from the latent space associated with the anomaly detection system.

A method implemented by one or more computer devices includes providing weakly-supervised neural networks for analysis of histopathology images. The method includes accessing a histopathology image including a slide-level class label. The method includes extracting a plurality of regions of pixels of the histopathology image at a plurality of magnifications. For each of the extracted plurality of regions of pixels, the method further includes inputting the region of pixels into a machine-learning model trained to generate a prediction of a class label for the region of pixels based on the region of pixels and the slide-level class label and outputting the prediction of the class label for the region of pixels. The method includes generating a prediction of one or more tile-level class labels for the histopathology image based on the predictions of class labels for each of the extracted plurality of regions of pixels.

A method for assessing nonalcoholic steatohepatitis (NASH) cirrhosis in a liver biopsy sample includes extracting, from the liver biopsy sample, image data indicative of one or more histopathological features, wherein the one or more histopathological features comprise septa and / or nodules and / or fibrosis, and analysing the extracted image data, using a machine learning model trained to assess the one or more histopathological features, to determine a degree of NASH cirrhosis. Training the machine learning model includes providing a plurality of training samples and a plurality of validation samples, each sample comprising a graded liver biopsy sample; quantifying parameters of the one or more histopathological features from image data of each of the training samples; selecting a subset of quantified parameters of the one or more histopathological features; constructing a model for assessing the one or more histopathological features from the subset of quantified parameters; and validating the constructed model using the validation samples.

Methods of obtaining a stained image of a biological sample using a single channel label -free fluorescence lifetime image of the sample comprising a fluorescence intensity image and associated fluorescence lifetime image as described. The methods comprise obtaining a single channel composite autofluorescence intensity and lifetime image from the single channel label -free fluorescence lifetime image, and providing the single channel composite autofluorescence intensity and lifetime image as input to a deep learning model that has been trained to take as input a single channel composite autofluorescence intensity and lifetime image and provide as output a corresponding stained image. Related methods, systems and products are also described.

The invention provides a traditional Chinese medicine preparation for treating allergic rhinitis and a preparation method thereof, and relates to the technical field of traditional Chinese medicine pharmacy, the traditional Chinese medicine preparation is prepared from radix asteris, schizonepeta spike, cynanchum paniculatum, perillaseed, spina gleditsiae, cottonrose hibiscus leaf, magnolia flower, semen brassicae, cortex lycii radicis, dendrobe, oroxylum indicum and rhodomyrtus tomentosa; an in-vitro experiment shows that the traditional Chinese medicine preparation can remarkably inhibit the activity of hyaluronidase, also can inhibit the release of beta-HEX in an RBL-2H3 cell degranulation model, reduces the HIS level in a cell supernatant, and effectively improves the morphological abnormality caused by degranulation of RBL-2H3 cells; in-vivo experiments show that the traditional Chinese medicine preparation can significantly reduce behavioral scores of mice suffering from allergic rhinitis, inhibit expression of inflammatory factors TNF-alpha, serum Ig E and OVE-sIg E levels and HA concentration, reduce histopathologic changes caused by allergic rhinitis, effectively repair immune injuries of nasal mucosa of the mice and improve the curative effect of the mice. And a safe and effective new strategy is provided for the treatment of allergic rhinitis.

A method for processing an electronic image including receiving, by a viewer, the electronic image and a FOV (field of view), wherein the FOV includes at least one coordinate, at least one dimension, and a magnification factor, loading, by the viewer, a plurality of tiles within the FOV, determining, by the viewer, a state of the plurality of tiles in a cache, and in response to determining that the state of the plurality of tiles in the cache is a fully loaded state, rendering, by the viewer, the plurality of tiles to a display.

Techniques for classifying, using a deep learning model, histopathological whole slide images (WSIs) as comprising images of cancerous or non-cancerous tissue and / or as comprising images of cancerous tissue having a genetic mutation or not having a genetic mutation are described herein. The techniques include at least one processor configured to instantiate a container-based processing architecture to train and / or use the deep learning model to process and classify at least one WSI. In some embodiments, a treatment may be selected and administered based on a classification result obtained from the deep learning model.

Methods and systems for analysing the cellular composition of a sample are described, comprising: providing an image of the sample in which a plurality of cellular populations are associated with respective signals and classifying a plurality of query cells in the image between a plurality of classes corresponding to respective cellular populations in the plurality of cellular populations. This is performed by providing a query single cell image to an encoder module of a machine learning model to produce a feature vector for the query image, and assigning the query cell to one of the plurality of classes based on the feature vector for the query image and feature vectors produced by the encoder module for each of a plurality of reference single cell images. The machine leaning model comprises: the encoder module, configured to take as input a single cell image and to produce as output a feature vector the single cell image, and a similarity module configured to take as input a pair of feature vectors for a pair of single cell images and to produce as output a score indicative of the similarity between the single cell images. Thus, the machine learning model can be obtained without the need for an extensively annotated dataset. The methods find use in the analysis of multiplex immunohistochemistry / immunofluorescence in a variety of clinical contexts.

The invention provides a medical tissue pathology panoramic slice image classification method and system, and relates to the technical field of medical image analysis, and the method comprises the steps: carrying out the segmentation of a panoramic slice image through employing an image segmentation basic model, and dividing the segmented slice image blocks belonging to each semantic region into an instance group; adopting a separation-combination pruning mechanism to screen representative instance groups, and performing iterative optimization until semantic division is stable; keeping the instance number of each instance group in proportion, and performing layered sampling to generate a pseudo packet; performing hybrid enhancement on pseudo packets from two different panoramic slice images, extracting a plurality of pseudo packets, and splicing the pseudo packets to form a hybrid pseudo packet; and taking the mixed pseudo packet as training data of a multi-instance learning model, and performing classification reasoning on an input panoramic slice image after training. According to the method, the accuracy and robustness of panoramic slice image classification can be remarkably improved, the dependence on pixel-level labeling is reduced, and the method has good practicability and universality.

A multimodal deep learning framework which is used to determine the likelihood of a particular treatment method effectively treating a patient with ovarian / kidney cancer with the goal of increasing patient survival. The framework takes into account not only large histopathology images (whole slide images), but also clinical variables to increase the scope of the data. The results demonstrate that the proposed models achieve high prediction accuracy and interpretability and can also be transferred to other cancer datasets without significant loss of performance. One of the key innovations here is the combination of pathology and clinical variables in a deep learning model to provide recommendations in therapy areas with limited information.

Presented herein are systems and methods for classifying features from biomedical images. A computing system may identify a first portion corresponding to an ROI in a first biomedical image derived from a sample. The ROI of the first biomedical image may correspond to a feature of the sample. The computing system may generate a first embedding vector using the first portion of the first biomedical image. The computing system may apply the first embedding vector to a clustering model. The clustering model may have a feature space to define a plurality of conditions. The clustering model may be trained using a second embedding vectors generated from a corresponding second portions with at least one of a plurality of image transformation. The computing system may determine a condition for the feature based on applying the first embedding vector to the clustering model.

The invention discloses a traditional Chinese medicine composition for preventing and treating alcoholic liver injury, and relates to the technical field of traditional Chinese medicines, and the traditional Chinese medicine composition is prepared from the following raw materials in parts by mass: 10 to 15 parts of radix puerariae, 6 to 12 parts of semen hoveniae, 6 to 10 parts of radix angelicae sinensis, 6 to 10 parts of fructus lycii and 2 to 5 parts of liquorice root. Based on the treatment rule of'dispelling effects of alcohol, regulating qi and blood and tonifying liver and kidney 'in traditional Chinese medicine, medicinal and edible traditional Chinese medicines such as radix puerariae and hovenia dulcis thunb are primarily screened, and meanwhile, a'multi-target synergistic mechanism' predicted by network pharmacology is constructed and converted into a quantifiable treatment effect. The experimental data not only verifies the key pathway of GO / KEGG enrichment, but also systematically verifies the compound liver protection effect through multi-scale indexes such as visceral organ indexes, serum enzymology, histopathology and the like. The research not only provides a novel compound with a clear mechanism for ALD, but also demonstrates the feasibility of driving the precise design of a traditional Chinese medicine compound by network pharmacology, and lays a methodological foundation for modern development of medicinal and edible resources.

This invention provides the application of dehydrouncitonin in the preparation of drugs that reduce polymyxin toxicity, belonging to the field of pharmaceutical technology. This invention uses dehydrouncitonin in combination with polymyxin. The combined use of dehydrouncitonin and polymyxin E increased the viability of HEK293T and RAW264.7 cells to 87.7% and 94.3%, respectively, with highly significant differences compared to the control, indicating that treatment with dehydrouncitonin in combination with polymyxin E can significantly inhibit the cytotoxic effect of polymyxin E. This invention uses a mouse model to demonstrate that treatment with dehydrouncitonin in combination with polymyxin E can significantly reduce the neurotoxicity and nephrotoxicity of polymyxin E alone in mice, specifically manifested in improved neurobehavioral effects and improved histopathological effects on brain and kidney tissues. This invention uses dehydrouncitonin in combination with polymyxin E to significantly inhibit the cytotoxic, neurotoxic, and nephrotoxic effects of polymyxin E.

Computer-implemented methods of analysing a histopathology sample are described, comprising obtaining a plurality of tile representations using a tile representation machine learning model, assigning each of the plurality of tile representations to one of a predetermined set of histomorphological phenotype clusters, obtaining a whole slide image representation using a histomorphological phenotype cluster language model, and predicting one or more biological or clinical features associated with the sample using a task specific machine learning model, wherein the task specific machine learning model is a model that has been trained using training whole slide images and optionally associated one or more ground truth biological or clinical features of interest to predict the one or more biological or clinical feature of interest for a whole slide image using as input the whole slide image representation provided by the histomorphological cluster language model for the whole slide image.

Provided is the processing of a large-scale colored image dataset by efficiently performing image analysis, processing, and manipulation based on adaptive particle representation (APR). 【Solution means】The analysis of large-scale biological objects and tissue samples is performed with a calculation speed 100 times or more faster and an excellent memory storage compression ratio. The embodiment is extremely suitable for large-scale imaging projects of large three-dimensional transparent tissue samples such as whole-brain mapping initiatives and human nerve tissue pathology. The data store holds an adaptive sampling representation for tiles, individual tile positions, and other corresponding image data for the biological object of interest. By reading out a tile from the data store only once, a complete image of the maximum resolution of the biological object is output as an image.

The application relates to a compound medicine for delaying skeletal muscle aging and application thereof, and the medicinal active components in the compound medicine composition are composed of menadione bisulfite sodium and N-acetyl cysteine, and belong to the technical field of biological medicine. A novel drug combination mode is developed, an oxidant menadione bisulfite sodium (MBS) and an antioxidant N-acetyl-L-cysteine (NAC) are combined and given in different time periods to regulate the H2O2 circadian rhythm of the skeletal muscle of old mice, the changes of the muscle strength, histopathology and aging marker protein level of the skeletal muscle of the old mice are detected, and then the precise compound medicine capable of improving the skeletal muscle function of the old mice and delaying skeletal muscle aging is determined. The application provides an effective drug combination strategy for relieving or treating the delay of skeletal muscle aging, and has very significant meaning.

The invention discloses an application of a compound fresh bamboo juice in a guinea pig asthma model, and relates to the field of medicines, a Hartley albino guinea pig is adopted to construct an OVA induced asthma model, and the model is verified to be successfully constructed through evaluation indexes; evaluating the efficacy of the successfully constructed guinea pig asthma model by using different doses of compound fresh bamboo juice, and evaluating the efficacy of the guinea pig asthma model by scoring guinea pig capsaicin stimulation cough inducing times, alveolar lavage fluid leukocyte number, inflammatory factor content, lung tissue hydroxyproline content and behavioral signs. And evaluating the treatment effect of the compound fresh bamboo juice through Western blot detection, lung histopathology and immunofluorescence staining indexes. In a guinea pig asthma model, high-dose and medium-dose groups of the compound fresh bamboo juice can be used for remarkably reducing the number of cough, reducing the number of white blood cells in alveolar lavage fluid and the level of inflammatory factors and inhibiting pulmonary fibrosis, and a good treatment effect is shown. The results provide powerful support for potential application of the compound fresh bamboo juice in asthma treatment.

The utility model relates to an automatic tool setting device for a wax block of a slicing machine, which belongs to medical laboratory equipment and is applied to the fields of medical clinical pathological histology, cytology, clinical examination, biological research and the like. The wax block automatic tool setting device has the advantages that the displacement sensor is installed in the middle of the side of the wax block chuck below the blade of the blade frame, automatic tool setting of the wax block is achieved through positioning signals and control of the displacement sensor, fingers are prevented from being cut or scratched in the manual tool setting process of the wax block, the accuracy of tool setting of the wax block is improved, and the speed of tool setting of the wax block is increased; and the practicability, flexibility and reliability are improved. Unnecessary manual operation steps are reduced, simplicity and rapidness are achieved, labor and time are saved, and the working efficiency is improved. Therefore, the method has great market prospect and economic value.

Techniques for classifying, using a deep learning model, histopathological whole slide images (WSIs) as comprising images of cancerous or non-cancerous tissue and / or as comprising images of cancerous tissue having a genetic mutation or not having a genetic mutation are described herein. The techniques include at least one processor configured to instantiate a container-based processing architecture to train and / or use the deep learning model to process and classify at least one WSI. In some embodiments, a treatment may be selected and administered based on a classification result obtained from the deep learning model.

The application discloses a lactobacillus johnsonii OD-5 and application and products thereof, and belongs to the technical field of microorganisms. A technical problem to be solved by the application is to provide a lactobacillus johnsonii capable of treating kidney stones. A technical solution is a lactobacillus johnsonii OD-5 with a preservation number of CGMCC No. 38426. The lactobacillus johnsonii can reduce urine oxalic acid and regulate urine calcium, effectively correct abnormal urine biochemical conditions promoting stone formation, reduce related kidney function damage, and inhibit formation and deposition of calcium oxalate crystals in the kidney at a histopathological level, thereby significantly reducing the stone incidence. The application provides a solid theoretical basis for development of products taking the lactobacillus johnsonii OD-5 as a core component and used for preventing kidney stones and improving stone kidney damage.

The invention relates to the technical field of medicines, in particular to application of a neuropeptide Y1 receptor antagonist in preparation of a hereditary polycystic kidney disease treatment medicine. The neuropeptide Y1 receptor antagonist is selected from a compound BIBO3304 which plays a role in blocking combination of neuropeptide Y (NPY) and a Y1 receptor subtype thereof. On the basis that early-stage histopathology, high-throughput sequencing, cytobiology and molecular biology are combined with in-vitro cell culture and in-vivo animal experiments, the potential treatment effect of the neuropeptide Y1 receptor antagonist in preparation of hereditary polycystic kidney disease treatment drugs is provided, and a new basis is provided for hereditary polycystic kidney disease treatment.

The present invention relates to the application of toxicogenomics methods to the detection and / or prediction of histopathological abnormalities in human or animal subjects based on clinical pathology data. It has been observed that reliable results can be obtained even in the absence of image data. A computer-implemented method for predicting the presence of a histopathological abnormality in an organ of a human or animal subject based on clinical pathology data comprises receiving clinical pathology data obtained from the human or animal subject, applying an analytical model to the clinical pathology data, the analytical model being configured to output a result indicative of the likelihood of the presence of the histopathological abnormality in the organ of the human or animal subject, and outputting the result.

This invention relates to a guinea pig model of chronic obstructive pulmonary disease (COPD) with chronic bronchitis, its preparation method, and its application. Specifically, this invention provides a guinea pig model of COPD with chronic bronchitis, established by subjecting guinea pigs to at least two intratracheal infusions of lipopolysaccharide (LPS) combined with repeated exposure to cigarette smoke. This results in the guinea pigs exhibiting pathophysiological characteristics consistent with those of COPD with chronic bronchitis in terms of cough sensitivity, airway inflammation, airway hypersecretion, lung function, and histopathology. This lays a solid foundation for in-depth research and drug screening of COPD with chronic bronchitis.

The invention discloses a method for establishing a mouse radioactive jawbone injury animal model, which comprises the following steps of: performing tooth extraction operation, performing radiation treatment after one day, and performing Micro-CT (Computed Tomography) scanning, intraoral examination and histopathologic detection on the lower jawbone on the thirteenth day after radiation so as to determine that the radioactive jawbone injury mouse model is successfully constructed. The animal model has the advantages that the pathological process of human early radioactive jaw necrosis is accurately simulated; the model is stable, good in repeatability and suitable for large-scale experimental research; and a reliable, short-cycle and convenient-to-operate animal model is provided for the mechanism research of radioactive jaw necrosis and the development of new drugs.

The present disclosure provides methods of training a deep learning (DL)-based classifier to classify a digital histopathology image with respect to a phenotype, methods of classifying a digital histopathology image with respect to a phenotype, and methods of identifying a biological mediator associated with a pathology, pathophysiology, or other clinical characteristic. The disclosure further provides systems for implementation of these methods.

The invention discloses an intelligent marine environmental pollution early warning method based on a biomarker, and belongs to the technical field of marine ecotoxicology, and the method comprises the steps: building an image quality control standard, obtaining a marine mussel histopathology image, building a data set according to the standard, carrying out the data marking and quality control, and carrying out the preprocessing. A model is constructed based on a YOLOv11 architecture, a multi-task segmentation function is realized by taking an image segmentation module as a core, and model training, verification and evaluation are carried out; different feature pixel points in the histopathology image are extracted through the trained model, then the shortest distance between the different feature pixel points is calculated through the Euclidean distance, and a result image containing distance marks is generated after averaging; and the marine environmental pollution early warning grade is divided by calculating the response index H. According to the method, the tissue pathology image is accurately analyzed by means of deep learning, identification and segmentation of different tissue structures of the marine mussels are realized, and an efficient technical means is provided for marine environment monitoring.

The present disclosure relates to a system and method for processing a digital pathological image using a machine learning model that includes a self-supervising hierarchical vision Transformer (ViT) configured to perform unsupervised clustering with a plurality of classification markers. The method includes receiving a digital pathological image depicting a tissue section stained with a histological dye. The digital pathology image may be processed to generate a plurality of predicted classification results including individual tiles of the digital pathology image. The result is generated by a machine learning model using a self-supervised hierarchical vision Transformer (ViT), which may further include a multi-head self-attention module configured to predict, for each individual tile in the digital pathology image, a cross-tile correlation metric using an attention mechanism, and to predict, for each individual tile in the digital pathology image, a cross-tile correlation metric based on the predicted cross-tile correlation metric. Thereby assigning the individual tiles to clusters based on the cross-tile correlation metric.

The invention relates to the technical field of artificial intelligence, and provides a lesion risk prediction method, a lesion risk patient management system, equipment, a medium and a product, and the method comprises the steps: obtaining a risk depth feature, a risk interaction feature and an original risk feature; and inputting the risk depth feature, the risk interaction feature and the original risk feature into a lesion risk prediction model to obtain a risk prediction result of the histopathologic precancerous lesion. According to the lesion risk prediction method provided by the invention, on the basis of the practical situation that precancerous lesion samples are few, prediction comprehensiveness is enhanced through multi-feature cooperation, hidden deep features and risk interaction features in risk depth feature capture data reveal an inter-variable association mechanism, and original risk features reserve original basic data; the three are combined to break through single feature limitation, more comprehensive feature knowledge is added to the prediction model, the generalization ability and prediction precision of the prediction model are enhanced, and thus the risk prediction accuracy of cervical histopathologic precancerous lesions is improved.

The invention provides a method for constructing a rat enterotoxicity model based on dysosma versipellis, and belongs to the technical field of animal models. The construction method of the rat enterotoxicity model comprises the following specific steps: 1) preparing a dysosma versipellis alcohol extract and a healthy rat; 2) carrying out gavage treatment on the healthy rats by using the dysosma versipellis alcohol extract; the dosage of the dysosma versipellis alcohol extract in the gavage treatment is 1.2 g / kg / d, and the gavage time is 3 days; and 3) after the rat is subjected to gavage treatment, judging whether the rat enterotoxicity model is successfully constructed according to the detection results of comprehensive toxicity expression, organ index change, histopathology and serum biochemical indexes of the rat. The method is simple in step and scientific and reasonable in evaluation index, the dysosma versipellis-based rat enterotoxicity model is successfully prepared, a theoretical basis is further provided for use of dysosma versipellis, and the blank of research in related fields is filled.