252 results about "Histopathology" patented technology

An example computer-implemented method for self-supervised training of an image processing model for histopathology images is provided. The example method includes obtaining a reference histopathology image; generating an augmented histopathology image, wherein generating the augmented histopathology image comprises performing, for an input image, at least one of the following augmentations: applying a blur to the input image and injecting noise artifacts into the blurred input image; or cropping a plurality of portions from the input image, wherein the plurality of portions are determined based on a minimum overlap criterion that has been updated over one or more iterations; and training the image processing model based on a similarity of latent representations generated by the image processing model respectively for the reference histopathology image and the augmented histopathology image.

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.

The invention belongs to the technical field of histopathology full-slice image analysis, and relates to a gene mutation prediction method and system based on a pathological report and full-slice cross-modal representation learning, and the method comprises the steps: obtaining a histopathology full-slice image and a data set of a corresponding pathological and molecular detection report, and carrying out the preprocessing and coding; performing word segmentation and feature extraction on the pathological diagnosis report and the molecular detection report; cross-modal representation learning is realized through reconstruction of pathological report features and comparative learning based on a category refinement queue; designing a loss function and training the model offline by using a sample data set with a label; and performing lung cancer gene mutation prediction on the histopathology full-slice image by using the trained model. According to the method, the defects that detailed annotations of histopathology full-slice image data are difficult to obtain and the single-modal information expression ability is insufficient can be overcome, and slice image level mutation tag classification is effectively completed by using a cross-modal information guide weak supervision method of a pathological report and a molecular detection report.

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.

The invention belongs to the technical field of biomedicine, and particularly relates to application of ceramide as a marker in prediction of hepatocellular carcinoma microvascular invasion risk. The invention finds that the preoperative plasma ceramide CER (d18: 1 / 16: 0) level can be used for predicting the MVI of HBV-HCC patients and evaluating the postoperative prognosis of the patients for the first time. The invention provides a preoperative non-invasive detection method based on plasma ceramide CER (d18: 1 / 16: 0), and trauma of traditional histopathology detection is avoided. According to the method, the occurrence risk of the MVI can be effectively reduced by simply detecting a blood sample of a patient. According to the method, the MVI risk of a patient is evaluated through a multi-factor prediction model, and a basis is provided for personalized treatment. The method is not only suitable for preoperative MVI risk assessment, but also can assist in making postoperative follow-up visit and treatment plans, and helps to reduce the postoperative recurrence rate and improve the survival rate of patients.

The invention belongs to the field of computer vision, and particularly relates to a histopathology full-slice image analysis system and method based on a double-branch cooperative training deep network model, and the method comprises the steps: obtaining a histopathology full-slice image data set with an image level label, and carrying out the segmentation preprocessing; block image coding based on a pathology basic large model; establishing a double-branch cooperative training deep network model based on the slice map; using a histopathology full-slice sample data set with an image level label to train a network model in an off-line manner; and predicting the histopathology full-slice image by using the trained network model, and outputting an analysis result of the full-slice image. According to the method, the correlation between the full-slice images is modeled by introducing the slice images to assist the deep network model, the defect that the correlation between the full slices is neglected in an existing histopathology full-slice image analysis method can be made up, and the histopathology full-slice analysis precision of the deep network model is improved.

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.

The invention belongs to the technical field of histopathology full-slice image analysis, and particularly relates to a histopathology full-slice image classification method and system based on a position coding converter, and the method comprises the steps: obtaining a pathological full-slice data set with an image and a class label thereof, and carrying out the segmentation preprocessing; using a feature encoder to extract block image features, and establishing a converter multi-instance learning network model based on position coding guidance; training a network model by using the pathological full-slice data set containing the weak label; according to the method, the problem that detailed annotations of pathological images are difficult to obtain can be solved through a multi-instance learning weak supervision method, and the importance of spatial and semantic relationships among different sub-regions in the pathological sections can be effectively considered; and completing a classification task of pathological full slices and carrying out visual interpretive analysis.

An MR Spectroscopy (MRS) system and approach is provided for measuring spectral information corresponding with propionic acid (PA), either alone or in combination with other measurements corresponding with other chemicals, to diagnose and / or monitor at least one of bacterial infection, such as associated with P. acnes, or conditions related thereto such as nociceptive pain associated with tissue acidity. An interfacing DDD-MRS signal processor receives output signals to produce a post-processed spectrum, with spectral regions corresponding with certain chemicals, including PA, then measured as biomarkers. A diagnostic processor derives a diagnostic value for each disc, and performs certain normalizations, based upon ratios of the spectral regions related to chemicals implicated in degenerative painful tissue pathology, such as PA and hypoxia markers of lactic acid (LA) and alanine (AL), and structural chemicals of proteoglycan (PG) and collagen or carbohydrate (CA).

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 self-supervised staining standardization method based on a de-noising diffusion probability model, which is mainly used for solving the problems of low robustness and generalization when a computer-aided system analyzes a histopathological image, and information loss, staining errors and the like existing in a current staining standardization algorithm. Compared with the prior art, by means of the denoising diffusion probability model and the self-supervising training method, staining standardization of histopathology images of various staining styles is achieved, the cell morphological structure is reserved, the accuracy of disease diagnosis of a computer-aided system in downstream tasks such as classification and segmentation is improved, and the method is suitable for popularization and application. And clinical practice and medical research promotion are facilitated.

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.

Diagnosing prostate cancer typically involves invasive measures such as biopsy in which tissue is removed and graded by pathologists. Non-invasive measures like multi-parametric MRI can be used to examine and grade lesions using the PI-RADS scale; however, the achievable image resolution is motion-limited. A new paradigm, magnetic resonance histopathology (MRH), is disclosed which aims to assess tissue texture at sub-millimeter resolution through rapid clinical acquisition procedures. MRH capability of identifying cancerous tissue in the prostate via an in silico analysis is demonstrated, recreating the MRH measurement process on a set of high resolution, pathologist-annotated histology slides. A dataset of spectral intensities at sub-millimeter wavelengths is created and a deep learning model trained to classify spectral data is underlying normal or tumor tissue. A set of spatial frequencies to optimize diagnostic power within the constraint of limited acquisition time is identified. Beyond single-region classification, the disclosed approach and architectures integrate spatial context and local information, the inclusion of which improves model performance and de-noises the inferential results. Applications of the trained models to unlabeled, high-resolution 3D MRI data is demonstrated in addition to algorithms for estimating the physical length scale of lesions identified by the model.

Computerized systems and methods for digital histopathology analysis are disclosed. In one embodiment, a series of deep learning networks are used that train, in succession, on datasets of successively increasing relevance. In some examples, learned parameters from at least a portion of one deep learning network are transferred to a next deep learning network in a succession of deep learning networks. In some examples, at least one of the deep learning networks includes a self-supervised learning network. In some examples, at least one of the deep learning networks includes an attention-based learning network. These and other examples and details are disclosed herein in various contexts including, for example evaluating genotypes of cancer tissue (e.g., bladder, prostate, or lung cancer) using histopathology images. In some examples, the context is to assist in predicting presence or absence of certain cancer genotypes and / or predicting patient responses to a new treatment.

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.

A computer-implemented method may include receiving a collection of unstained digital histopathology slide images at a storage device and running a trained machine learning model on one or more slide images of the collection to infer a presence or an absence of a salient feature. The trained machine learning model may have been trained by processing a second collection of unstained or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained or stained digital histopathology slide images of the second collection. The computer-implemented method may further include determining at least one map from output of the trained machine learning model and providing an output from the trained machine learning model to the storage device.

The invention discloses a tumor diagnosis method and system for generating a molecular spatial distribution diagram based on MRI (Magnetic Resonance Imaging), and belongs to the technical field of image data processing. The method comprises the following steps: constructing an MRI-pathological local matching data set; the Transform and the GAN are combined to construct a local MRI (Magnetic Resonance Imaging) to generate a small pathological graph model; and constructing and verifying a molecular spatial distribution diagram. According to the method, the histopathology is taken as a bridge, a two-layer mapping relation is constructed between the MRI and the molecular features, the global molecular features are estimated through local matching data, existing clinical resources are effectively utilized, and three-dimensional space distribution evaluation of the molecular features is indirectly achieved at low cost. And the constructed model can be used for pathological diagnosis in any region on the brain MRI sequence, so that the potential invasion region of the tumor can be explored, and reference is provided for the surgical resection range definition and treatment strategy planning of the glioma.

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.

The invention provides a bladder cancer gene mutation prediction system based on deep learning, and the system comprises a histopathologic image collection module which is used for collecting an original to-be-detected histopathologic image; the preprocessing module is used for preprocessing the original to-be-detected histopathologic image by adopting an HE staining method and a full-slide imaging scanner to obtain WSI with HE stained bladder tissue; the cloud server module is used for calling a bladder cancer deep learning prediction model to perform bladder cancer gene expression state prediction, bladder cancer gene methylation state prediction and bladder cancer gene mutation state prediction on the WSI to generate corresponding prediction results; and the report generation module is used for automatically generating a bladder cancer gene mutation prediction auxiliary diagnosis report according to the corresponding prediction result and feeding back the report to the specified terminal.

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.

The invention discloses a method for constructing a human marrow paraffin tissue space transcriptome sequencing map, which comprises the following steps of: obtaining high-quality space transcriptome data from a marrow paraffin section of an AML (acute myeloid leukemia) patient, and then integrating the space transcriptome with scRNA-seq and histopathology. Relatively complete transcriptome information is still preserved after the bone marrow specimen is subjected to better decalcification treatment, and meanwhile, higher-quality slicing treatment can be performed on the embedded paraffin tissue, so that very complete bone marrow space transcriptome sequencing data is obtained. Then, the cell type of each detection site in the bone marrow space transcriptome map can be accurately defined by integrating bone marrow single cell sequencing data and pathological morphology annotations, and subsequent analysis is carried out.

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.