194 results about "Digital pathology" patented technology

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention belongs to the technical field of medical image processing, and particularly relates to an esophageal cancer treatment effect and survival combined prediction method and system based on multiple modalities, and the method comprises the steps: obtaining a preoperative CT image and Hamp of an esophageal squamous cell carcinoma patient; e, the dyed digital pathological image, transcriptome data and clinical diagnosis and treatment information are preprocessed and subjected to feature extraction, and radiomics embedding representation, pathomics embedding representation and pipeline branch embedding representation are obtained respectively; the radiomics embedded representation, the pathomics embedded representation and the pipeline branch embedded representation are aligned and input into a multi-modal fusion module based on a multi-head self-attention mechanism for feature fusion, and fusion feature representation is generated; and based on the fusion feature representation, synchronously outputting a curative effect prediction result and a survival prediction result by using a multi-task output module, and evaluating model prediction performance by using a curative effect evaluation index and a survival analysis index.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention discloses a pathological image sjogren syndrome automatic diagnosis method, device and equipment and a storage medium, and relates to the technical field of medical data processing, and the method comprises the steps: carrying out the image preprocessing of an original digital pathological section image, and obtaining an optimized image block; performing cell-tissue collaborative segmentation on the optimized image blocks to obtain a cell entity set and a tissue entity set; the cell entity set and the tissue entity set are mapped into a heterogeneous graph structure, and the heterogeneous graph structure takes cell graph nodes and tissue graph nodes as vertexes and takes a spatial adjacency relation as edges; and performing sicca syndrome state judgment based on the heterogeneous graph structure. According to the method, the heterogeneous graph structure is constructed, the cells and the tissues are abstracted into the nodes, the spatial adjacency relation edges are established, modeling of the global relation between the cells and the cross-scale relation between the cells and the tissues is achieved, global topology perception is achieved, the diagnosis accuracy is improved, and an end-to-end intelligent analysis process from a pathological image to sicca syndrome diagnosis is formed.

The invention relates to the technical field of digital pathology acquisition, and discloses a digital pathology picture acquisition method. The method comprises the following steps: acquiring sample characteristic data such as thickness, dyeing degree and structural characteristics of a tissue sample to be detected, and determining initial image acquisition parameters including exposure time, image resolution and optical magnification; and meanwhile, environment condition data such as temperature, humidity and environment illumination intensity of a scanning environment are collected, so that whether the initial parameters are adjusted or not is judged. When adjustment is needed, collecting preliminary image data, extracting quality features including image definition, color accuracy and detail retention degree, calculating feature values, and comparing the feature values with historical image quality feature values in a historical database; and if the same characteristic value does not exist in the historical database, calculating the similarity, and adjusting the initial image acquisition parameters according to the comparison result or the similarity. According to the method, sample characteristics and environmental conditions are comprehensively considered, and the quality of the digital pathological picture is effectively improved.

The invention provides a full-slice image classification method and system based on multi-branch attention and random instance masks and a medium, and the method comprises the steps: obtaining a full-slice digital pathological image, carrying out the image segmentation, and obtaining a plurality of instances; performing feature extraction based on a feature extraction network to obtain an instance feature sequence; carrying out parallel analysis on the instance feature sequence based on a multi-branch attention mechanism, executing a random Top-K instance mask operation, and carrying out weighted summation on the instance feature sequence based on the attention distribution of each attention branch; aggregating the packet level feature representations of all the attention branches, generating a comprehensive feature representation of the full-slice digital pathological image, and obtaining a classification prediction result; by setting a plurality of parallel attention branches, different branches are promoted to actively learn and capture a plurality of different visual modes existing in the full-slice digital pathological image, so that tumor heterogeneity can be effectively represented, and the generalization ability of full-slice digital pathological image classification is improved.

The invention provides a digital pathological image high-color accurate splicing method and device based on feature point matching. The method comprises the following steps: obtaining a target splicing region in a target splicing image and a to-be-spliced region in a to-be-spliced image based on a scanning sequence of digital pathological images; performing affine transformation on the to-be-spliced region by using the affine transformation matrix to obtain an initial correction region; constructing a global color mapping function, and mapping the initial correction image by using the global color mapping function to obtain a color correction image; and splicing the color correction image and the target splicing image based on the scanning sequence of the pathological images to obtain a digital pathological image splicing result. According to the scheme, only the target splicing area and the to-be-spliced area are subjected to feature point matching, the cumulative distribution function of the effective feature points is calculated according to the RGB channels, and the global color mapping function is constructed and optimized, so that accurate color correction is realized, and the visual integrity and consistency of the spliced image are improved.

The invention belongs to the field of digital pathological image processing, and discloses an OpenSlide-based digital pathological image processing method and a related device, and the method comprises the steps: obtaining a digital pathological image, calling an OpenSlide library to analyze a pyramid structure of the image to generate a multi-resolution hierarchy, and dynamically calculating a loaded image region based on a slice request instruction input by a user; a double-Canvas rendering mode is adopted, a bottom layer Canvas loads an image area in real time, a labeling layer Canvas independently renders a labeling path, and separation of an image and a label is achieved; and generating a closed path based on the superposed image and the annotation mode, and exporting the annotation data in a general format. According to the method, the user interaction experience is improved, the interface operation is more intuitive, the process is simplified, and the drawing mode switching is convenient; the labeling efficiency is improved, real-time adjustment of complex contours is supported, and the workload is reduced; a data export function is enhanced, effective association of annotation information is realized, and multi-format universality is supported; multi-level support is improved, level switching is smooth, loading is fast, and tool performance and user experience are improved.

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.

Techniques for using combination stain types for machine learning models for digital pathology are described herein. In an example, a system accesses a first image of a sample comprising an immunohistochemistry (IHC) stain for a biomarker. The system accesses a second image of the sample comprising a hematoxylin and eosin (H&E) stain for nuclei. The system can segment tissue regions in the one or more first images and the second image, partitions the tissue regions in the one or more first images and the second image, and extracts features from the set of tiles using a feature extractor. The system can generate, by a machine-learning model, an output classification indicating a first phenotype based on the features extracted from the set of tiles. The machine-learning model can include one or more classifiers and an aggregation model that provides an aggregated output for the set of tiles.

The embodiment of the invention discloses a gastric biopsy pathological risk degree hierarchical identification processing method and system, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: obtaining a digital pathological image of a to-be-diagnosed gastric biopsy pathological section; analyzing the digital pathological image based on an artificial intelligence classification model to obtain a risk level corresponding to the digital pathological image; distributing the digital pathological image to a preset diagnosis path matched with the risk level; wherein the preset diagnosis paths corresponding to different risk levels are different in detail degrees of auxiliary diagnosis information provided by the system or triggered automatic auditing processes; receiving a diagnosis result of manual examination and verification for the digital pathological image, and using the diagnosis result as feedback data for updating the artificial intelligence classification model; through automatic risk layering and differentiated path distribution, diagnosis resources can be optimized, diagnosis efficiency and accuracy can be improved, and continuous evolution of the model can be realized.

The invention relates to the field of digital pathology, and provides a digital slice quality control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a highest magnification tile of a digital slice to be subjected to quality control; based on a trained pathological defect segmentation model, carrying out pathological defect identification and segmentation on the highest-magnification tile to obtain a highest-magnification mask graph; performing pixel-level statistics on the highest-magnification mask image to obtain a single-tile quality control result, and summarizing the single-tile quality control result to obtain a global quality control result of the digital slice; and based on the highest multiplying power mask graph, reconstructing a pyramid mask graph containing multiple multiplying power hierarchies by adopting a plurality of parallel processes, and performing rechecking based on the global quality control result and the pyramid mask graph. According to the digital slice quality control method and device, the electronic equipment and the storage medium provided by the invention, the efficiency bottleneck problem of the quality control process is effectively solved through the technical innovation of reconstructing the pyramid mask graph by the parallel process.

The invention discloses a multi-slice scanning device and method, and relates to the technical field of digital pathology, and the multi-slice scanning device comprises a slice bin, a slice taking and returning mechanism, a macroscopic image collection mechanism and a microscopic scanning mechanism. The slice bin is used for storing slices; the microscopic scanning mechanism is arranged on one side of the slice bin and is used for scanning the slices; the macroscopic image acquisition mechanism is arranged between the microscopic scanning mechanism and the slice bin and is used for identifying a specimen area on the slice before scanning; the slice taking and returning mechanism is arranged on one side of the microscopic scanning mechanism and used for conveying the slices in the slice bin to the macroscopic image collecting mechanism firstly and then conveying the slices to the microscopic scanning mechanism, and finally the specimen areas of the slices are exposed to the scanning area of the microscopic scanning mechanism. The slice taking and returning mechanism is further used for conveying the scanned slices back to the slice bin. According to the designed multi-slice scanning device, the number of moving parts can be reduced, the mechanical structure is simplified, and therefore the reliability and the whole-process speed are improved, and the increasing high-throughput scanning requirement is met.

A computer-implemented method of reviewing digital pathology data may include receiving a digital pathology image into a digital storage device, the digital pathology image being associated with a patient, providing for display the digital pathology image on a display, pairing the digital pathology image with a physical token of the digital pathology image in an interactive system, receiving one or more commands from the interactive system, determining one or more manipulations or modifications to the displayed digital pathology image based on the one or more commands, and providing for display a modified digital pathology image on the display according to the determined one or more manipulations or modifications.

This invention relates to an intelligent decision support system for rectal cancer prognosis integrating digital pathological images, belonging to the field of medical image processing technology. The system includes a data acquisition module for collecting multiple sets of sample data; a feature selection module for identifying multiple key medical imaging features of rectal cancer based on the multiple sets of sample data; a model building module for acquiring a pre-trained deep learning model and constructing a rectal cancer prognosis prediction model based on transfer learning, the pre-trained deep learning model, the multiple sets of sample data, and the multiple key medical imaging features of rectal cancer; an image acquisition module for acquiring pre- and post-operative medical images of the rectal cancer patient to be evaluated; and a recurrence prediction module for predicting the recurrence probability of the rectal cancer patient to be evaluated based on the pre- and post-operative medical images of the patient using the rectal cancer prognosis prediction model. This system has the advantage of improving the accuracy of non-invasive prediction of postoperative recurrence of rectal cancer.

The application provides a digital pathological image high-color-precision splicing method and device based on feature point matching, which comprises the following steps: obtaining a target splicing area in a target splicing image and a to-be-spliced area in a to-be-spliced image based on the scanning sequence of the digital pathological image; performing affine transformation on the to-be-spliced area by using an affine transformation matrix to obtain an initial correction area; constructing a global color mapping function, and mapping the initial correction image by using the global color mapping function to obtain a color correction image; and splicing the color correction image and the target splicing image based on the scanning sequence of the pathological image to obtain a digital pathological image splicing result. According to the scheme, feature point matching is only performed on the target splicing area and the to-be-spliced area, the cumulative distribution function of the effective feature points is calculated respectively according to RGB channels, and an optimized global color mapping function is constructed, so that accurate color correction is realized, and the visual integrity and consistency of the splicing image are improved.

This invention discloses a lung cancer pathological image classification method based on topological relationship fusion, belonging to the field of intelligent medical image diagnosis and digital pathology analysis technology. To address the problems of existing methods, such as fixed segmentation leading to the fragmentation of key structures, neglect of inter-regional topological relationships, and lack of interpretability, the present invention includes: acquiring lung cancer pathological images and preprocessing them to form a sample set; constructing a pathological unit extraction module to adaptively filter local pathological units; constructing a topological relationship fusion module to model the spatial adjacency and distribution relationships of each pathological unit, fusing local and topological features to obtain a global representation; constructing a prototype matching decision module to establish pathological prototypes for different lung cancer categories, achieving classification through similarity matching and outputting key support regions; and finally, inputting the test set into the trained classification model to obtain the predicted category. This invention improves classification accuracy, stability, and interpretability through pathological unit extraction and topological relationship modeling.

The invention relates to a novel HR positive HER2 negative breast cancer typing system and a novel HR positive HER2 negative breast cancer typing method, and the HR positive HER2 negative breast cancer is divided into four subtypes, namely a classical cavity surface type, an immune regulation type, a proliferation type and a receptor tyrosine kinase driven type, according to multiple omics results of the HR positive HER2 negative breast cancer. Different treatment schemes are adopted for the four subtypes respectively, and accurate treatment of different subtypes of HR positive and HER2 negative breast cancer can be achieved. The invention also relates to an HR positive HER2 negative breast cancer typing method based on AI digital pathology or classifier gene expression.

A digital pathology image collected using bright-field imaging that depicts a slide with a stained sample slice is accessed. A stain intensity that corresponds to at least part of the digital pathology image is detected. A biomarker-intensity-prediction function that linearly relates predicted biomarker-intensity levels to detected stain intensities is accessed. A non-linear confidence function is accessed that relates confidences of a predicted biomarker intensity to the detected intensities of the stain. A predicted biomarker intensity is generated for the at least part of the slide using the detected stain intensity that corresponds to the at least part of the slide and the linear biomarker-intensity-prediction function. A confidence metric for the predicted biomarker intensity is generated using the detected stain intensity that corresponds to the at least part of the slide and based on the confidence function. A result based on the predicted biomarker intensity and the confidence metric is output.

This platform is a privacy-preserving, federated learning system designed to integrate whole-slide digital pathology images with matched molecular profiling and clinical metadata for improved diagnosis, subtyping, and prognostic estimation of gynecologic tumors. The architecture comprises local institutional nodes that retain raw patient data while participating in distributed model training coordinated by a central orchestration server. Each local node preprocesses whole-slide images into patch-level tensors, extracts visual embeddings via convolutional backbones, and processes molecular vectors (e.g., somatic mutations, expression summaries, copy-number measures) via a molecular encoder. A multimodal fusion module - implemented as an attention- based transformer - integrates image and molecular embeddings into a unified representation used by multi-task heads for classification (histologic subtype, diagnostic label) and regression (risk score). The federated learning controller aggregates encrypted model updates (FedAvg) and returns improved global weights without exchanging raw data, enabling cross-site generalization while preserving patient privacy. Explainability components generate attention maps and tile-level saliency (Grad-CAM style) linked to molecular features, providing interpretable morpho- molecular correlations to pathologists. The platform supports API integration with PACS / LIMS, conforms to privacy standards via optional differential privacy and secure aggregation layers, and is extensible to additional omics modalities or transfer / fine-tuning workflows for related tumor types. By combining multimodal fusion, federated training, and clinician-facing interpretability, the system accelerates robust, generalizable AI for precision pathology in gynecologic oncology.

The invention belongs to the technical field of digital pathology and fluorescence imaging, and relates to a fluorescence pathological section scanning system and a scanning method thereof. The system comprises a fluorescence imaging module, a scanning displacement table for bearing and driving a pathological section to perform two-dimensional scanning under a microscope objective, and a protective gas supply module comprising a protective gas storage or generation device, a gas pipeline and a protective gas nozzle. The protective gas spray head is arranged above the scanning displacement table and located near the optical axis of the microscope objective, protective gas with stable chemical properties is continuously sprayed out towards the focus area of the microscope objective in the scanning process, a low-oxygen-content area is formed on the surface of a section, photooxidation quenching or bleaching of fluorescent dye is effectively inhibited, and the service life of the section is prolonged. And the brightness uniformity and the quantitative analysis reliability of the fluorescence image under long-time and multi-view splicing scanning are improved. The invention further discloses a corresponding single-piece and multi-piece continuous fluorescent pathological section scanning method.

The application discloses a kind of pathological image classification methods based on dyeing perception intelligent feature modeling, belong to pathological image classification field, this method includes collecting the multi-staining full section image group data of different objects;Multi-staining full section image group data includes H&E dyeing image and several immunohistochemical dyeing images;Each multi-staining full section image group data is respectively preprocessed, obtains the several equal-size image blocks of each object;Based on the image block of each object, graph is constructed, and the multi-modal heterogeneous pathology graph of each object is obtained;With the multi-modal heterogeneous pathology graph of each object as training data, train pathological image classification model;The pathological image classification model of training completion is used to classify the pathological image to be measured.The application solves the problems of the existing method in processing digital pathology graph data, such as node category characteristics, edge information utilization, cross-staining interaction and non-perfect data processing.

The embodiment of the invention discloses an intelligent recognition and grading method for gastric intestinal metaplasia, and the method comprises the steps: collecting a gastric mucosa HE staining digital pathological image, and building a universal part recognition model through a convolutional neural network according to the pathological classification standards of different parts of a stomach body, gastric antrum and gastric horn. In order to reduce judgment errors caused by other glands, a semantic segmentation model is trained, and the gastric mucosa is accurately found out. On the basis, a developed high-precision detection and recognition algorithm is used for recognizing the enteric glands, and quantitative determination is carried out on the enteric glands in the gastric mucosa of the stomach body, the antrum and the corner of the stomach. And the identified target glands are automatically counted, and intelligent OLGIM grading is carried out.

Systems and methods for predicting the therapeutic response of a specified disease therapy for individual patients based on an analysis of digital pathology images are described. In some instances, for example, the disclosed methods can comprise: receiving an image of a tumor specimen from a patient; segmenting the image to identify tumor cell nuclei; generating a feature vector that includes a plurality of features, each corresponding to a statistical measure of one of a set of morphological parameters used to characterize the tumor cell nuclei; and providing the generated feature vector as input to a trained machine-learning model configured to output a prediction of the therapeutic response of the specified disease therapy for the patient.

The present disclosure provides a method for collaborative review of digital pathology images. The method may include initiating a collaboration session including a plurality of practitioners on an electronic network, receiving an indication of one or more digital pathology images to be reviewed in the collaboration session from a first session instance associated with a first practitioner, providing the indication to at least a second session instance associated with a second practitioner, receiving viewing data including zoom level, viewport rotation, and cursor coordinates associated with the first session instance, the viewing data not including video data, and providing the viewing data to at least the second session instance.

The application discloses a fine segmentation method for prostate and internal lesion areas based on large pathological sections, and specific steps are as follows: step 1, sequentially performing fixation, paraffin embedding, continuous transverse sectioning and HE staining operations on the whole tissue; step 2, extracting an HE staining image; step 3, sequentially scanning the pathological sections in step 2 into digital pathology; step 4, processing the digital pathology; step 5, performing image registration and three-dimensional image reconstruction on analysis results of multiple pathological sections processed in step 4; step 6, training a magnetic resonance multi-modal sequence segmentation model; step 7, extracting features of normal prostate tissue and lesion tissue in three sequences; step 8, acquiring high-risk factor information and quantifying features; step 9, constructing a sample feature matrix; and step 10, predicting the malignancy degree of prostate cancer. The method can realize more accurate benign and malignant evaluation of prostate lesions and prediction of the malignancy degree of prostate cancer.

The invention provides a liver cancer capillary density evaluation system based on a CD34 and GPC-3 combined marker. The liver cancer microvessel density evaluation system based on the CD34 and GPC-3 combined marker comprises a digital pathology scanning module, a tumor area recognition module, a microvessel feature extraction module, a spatial correlation analysis module, a molecular subtype integration module, a microvessel invasion evaluation module, a prognosis prediction module, a dynamic layering module, a report generation module and a treatment recommendation module. The liver cancer microvessel density evaluation system based on the CD34 and GPC-3 combined marker has the advantages that the limitation of single quantification of microvessels is broken through, multi-dimensional accurate analysis of the liver cancer microenvironment is achieved through blood vessel function typing and spatial topology modeling, and conversion from pathological diagnosis to individualized treatment is promoted.

The application discloses a kind of kidney cell cancer patient prognosis risk prediction methods based on multi-modal deep learning.The present application processes whole field digital pathology section (WSI) image and pathological diagnosis report by deep learning algorithm, extracts text features using natural language processing model, extracts image features using computer vision model and ABMIL network, and carries out multi-modal feature fusion through Cross-attention Transformer network, and finally outputs the survival risk curve of patient changes with time in combination with Weibull survival function, for assisting doctor to carry out more accurate prognosis evaluation and survival analysis.

The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and / or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and / or intensities in the input image, wherein each sub-region represents biologically meaningful data.

Techniques for using a digital pathology machine learning model implementation without requiring transmission of digital pathology images to a location of the digital pathology machine learning model implementation are presented. The techniques may include: providing, on a server computer, a digital pathology image embeddings API; receiving, from a client computer, an embeddings job request including an identification of at least one digital pathology image, an image resolution instruction, and an identification of an embeddings network; passing, to an embeddings server, metadata characterizing the digital pathology image(s) resolved according to the resolution instruction; obtaining, from the embedding server, at least one embeddings vector after the embeddings server transforms a resolved set of digital pathology image(s) into at least one embeddings vector; and transmitting, by the server computer, the embeddings vector(s) to a storage location, without the client computer transmitting or receiving the digital pathology image(s).