245 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 discloses a renal clear cell carcinoma prognosis prediction method based on multi-mode MRI (Magnetic Resonance Imaging) image and digital pathological omics fusion. The method comprises the following steps: S1, collecting a training data set based on an MR image and a pathological image; s2, feature extraction of MR radiomics; s3, deep learning feature extraction of the pathological image; s4, an MR-pathological feature fusion module; and S5, deploying the network. According to the method, depth features with prognosis information are obtained from two scales of pre-treatment images and post-operation pathology, effective features are extracted by adopting image omics and a convolutional neural network mode according to data characteristics of MR images and pathology images, and depth fusion of the two types of features is completed in a hidden space through a multi-task guiding mode, so that the accuracy of the MR image and the pathology image is improved. A precise prognosis model with multi-scale information is provided, and the method has a relatively strong clinical application prospect and is of great significance for realizing precise immunotherapy and improving prognosis of a patient.

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 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 belongs to the field of digital pathological image processing, and particularly relates to an intelligent auxiliary diagnosis system and method based on cell contours. The intelligent auxiliary diagnosis method based on the cell contour comprises the following steps: S10, acquiring a stained pathological tissue image, separating an HE staining channel in the pathological tissue image through a color deconvolution algorithm, and identifying the position of a cell nucleus in a marked image according to a separation result as an initial position; calculating the staining intensity of each cell nucleus according to the separation result, constructing a contour characteristic spectrum based on the staining intensity of the cell nucleus, and extracting tissue microenvironment indication information in the pathological tissue image; s20, defining a dyeing concentration peak region as a first core region, and defining a cell nucleus edge diffusion region as a second peripheral region; and combining the geometrical morphology characteristics of the cell nucleus with the gradient distribution of the dyeing intensity. According to the scheme, the cells in the pathological tissue image can be accurately segmented and classified, a quantitative diagnosis basis can be provided, and the working pressure of doctors is relieved.

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.

Described herein are systems, methods, and programming for analyzing and classifying digital pathology images agnostic to sample processing techniques used to prepare the digital pathology images. In some embodiments, image data including a first image set and a second image set may be obtained. The first and second image sets may be processed using a first and second slide preparation machine, respectively. A first augmented view set and a second augmented view set may be generated based on augmentations applied to the first and second image sets. For each image, a first vision transformer to may be trained to: generate a first representation of an augmented view of the first augmented view set, and enhance a similarity between the first representation and a second representation of an augmented view of the second augmented view set. The second representation may be generated via a second vision transformer.

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 provides a digital pathological section color calibration method and device, and the method comprises the following steps: obtaining a standard color image and an image to be subjected to color correction, and extracting the feature points of the standard color image and the feature points of the image to be subjected to color correction, matching the standard color image and the image to be subjected to color correction based on the feature points to obtain a standard color matching image and a matching image to be subjected to color correction; performing histogram color correction on the matching image to be subjected to color correction by taking a standard color matching image as a reference to obtain a first color correction image, and performing CCM color correction on the first color correction image by taking the standard color matching image as the reference to obtain a second color correction image, and performing gamma correction on the color of the second color correction image by taking a standard color matching image as a reference to obtain a color correction completed image. According to the scheme, all pixel information of the target image is fully utilized, the color correction process is quantified to eliminate artificial errors, and the efficiency and accuracy of color correction of the digital pathological image can be remarkably improved.

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 provides a digital pathological section auxiliary diagnosis warning device and method, and the device comprises a first region obtaining module which is used for obtaining a section warning region, and the predicted cancer probability of the section warning region is included in a cancer-free probability interval; the second area acquisition module is used for collecting a local area of which the predicted cancerous probability is within a cancerous probability interval in the slice warning area; the warning island display module is used for displaying a warning island in the slice viewing interface, and the warning island comprises a thumbnail of a local area; and the visual field following module is used for acquiring the target thumbnail selected by the user and displaying the slice visual field corresponding to the target thumbnail to the user. According to the digital pathological section auxiliary diagnosis warning device and method, the local area with the predicted cancer probability in the cancer probability interval in the section warning area is collected, and the corresponding thumbnail is displayed in the warning island, so that a user can conveniently check all high-risk tiny cancer lesions in a unified manner, the diagnosis accuracy of the section is improved, and missed diagnosis is avoided.

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 discloses a hierarchical optimization multi-instance learning method for digital pathological section brain tumor classification. The hierarchical optimization multi-instance learning method comprises the following steps: extracting and aggregating multi-instance features through alternate training of a feature encoder and an aggregator; a region of interest is automatically searched in a low-magnification image, and then feature extraction and classification are performed on a high-magnification region of interest. According to the method, the region-of-interest is detected by using low magnification, and then the tumor category is comprehensively judged in multiple magnification near the region-of-interest in stages. More importantly, in a model training link, a feature extractor is trained by utilizing a pseudo tag of patch in a region of interest, so that features are better extracted for a specific task and a data set. In order to reduce the noise of the false label, the patent provides a label correction mechanism to ensure the purity of the false label. In general, the HOMIL combines low-magnification tissue structure characteristics and high-magnification cell characteristics, comprehensively predicts brain tumors, and greatly reduces the model reasoning time.

A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.

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 pathological image full-automatic segmentation and classification method and system based on deep learning, and the method comprises the steps: selecting a to-be-processed tissue slice image, extracting the feature information of a cell nucleus in the to-be-processed tissue slice image, and carrying out the initialization processing; the unified feature decoder is introduced to process the image, and different feature information is integrated; carrying out instance segmentation on the cell nucleus by adopting a convolutional neural network, and optimizing a feature extraction process in combination with a self-attention mechanism and a residual network; meanwhile, a classification module is used for carrying out category prediction on cell nucleuses, and tissue region guide information is fused; and carrying out comprehensive optimization on the segmentation result and the classification result through multi-task joint training until a preset stop condition is met, and obtaining a high-quality cell nucleus segmentation and classification result. The method can automatically and efficiently perform cell nucleus segmentation and classification in the digital pathological image, has high accuracy and stability, is suitable for various complicated tissue slice analysis tasks, and does not need manual intervention.

The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.

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.

An automatic recognition method includes the following steps: collecting multiple digital pathology slide images as sample data, segmenting collected sample image data, where segmented cell images includes a positive cell image and a negative cell image, and the positive cell image and the negative cell image obtained by segmentation are stored into a positive cell image set and a negative cell image set correspondingly; preprocessing images in the two image sets to facilitate subsequent recognition and extraction of a single cell picture in the image; acquiring the extracted single cell picture, extracting a feature of a single cell image to be recognized, and training an initial neural network with a corresponding cell feature as a label; and generating a comprehensive evaluation coefficient according to the cell feature corresponding to the image cell in each region, and determining a detailed cell type according to the comprehensive evaluation coefficient.

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.