256 results about "Pathology diagnosis" patented technology

The invention relates to the technical field of pathological image analysis and mining, and particularly discloses an ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, and the method comprises the following steps: S1, collecting a tissue pathological image of an ovarian cancer patient and a corresponding full-view digital pathological image; and S2, generating a multi-view data set. According to the ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, the problem that in the prior art, patch-level labels are generally lacked in the field of multi-instance pathological images, so that many natural image processing methods cannot be applied to the field of pathological images is solved. A ResNet backbone network is used for extracting features of pathological images under the maximum magnification, a small number of pathology prototypes are introduced to guide deep embedded clustering through pathology expert priori knowledge, a pathology image spectrogram is introduced to serve as a reference view, and the accuracy and stability of clustering are enhanced.

The invention relates to a pathological section intelligent auxiliary differential diagnosis system based on multi-modal fusion, in particular to the field of clinical pathology, semantic unification of multi-modal data is achieved through meta-task construction and a cross-modal alignment technology, and transferable diagnostic knowledge is extracted by utilizing a meta-learning framework; the method combines a generative model and knowledge constraints to generate high-quality synthetic data, and finally fuses real and synthetic samples through a self-adaptive diagnosis mechanism, thereby remarkably improving the differential diagnosis capability of rare lesions, effectively solving the problem of model generalization in a training data scarcity scene, and improving the accuracy of model identification. And efficient and reliable intelligent auxiliary decision support is provided for clinical pathological diagnosis.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention discloses a personalized health management method and system based on an AI electronic medical record, and relates to the technical field of artificial intelligence medical treatment, and the method comprises the steps: analyzing an original electronic medical record to generate a personal health timeline; carrying out feature extraction on the health state evolution sequence, identifying key nodes, and carrying out pathological labeling according to a medical knowledge graph to form a health state evolution sequence with a pathological label; a risk assessment model is constructed, future disease risks are calculated based on the sequence, and a dynamic report is generated; making a personalized health management plan in combination with the living habits and genetic backgrounds of the users; during plan execution, user feedback and monitoring data are collected in real time, and plan content and strength are dynamically adjusted by using a reinforcement learning mechanism. According to the method, the medical interpretability of health state evolution is enhanced through pathological labeling, and dynamic closed-loop optimization of a management plan is realized through reinforcement learning.

Techniques of automated quality control for digital pathology whole slide images are presented. The techniques include obtaining a thumbnail image derived from a whole slide image of a pathology slide; determining whether the whole slide image includes an artifact in a first class of artifacts by providing the thumbnail image to an electronic neural network trained to detect artifacts in the first class of artifacts by analyzing a plurality of labeled training thumbnail images; generating a tissue mask representing tissue depicted in the thumbnail image; determining whether the whole slide image includes an artifact in a second class of artifacts by performing a comparison using the tissue mask; and providing an indication of whether the whole slide image includes an artifact in the first class of artifacts or an artifact in the second class of artifacts.

The present disclosure provides systems and methods for detecting anti-NMDAR autoantibodies based on the strong affinity of the anti-NMDAR autoantibodies to a plurality of non-random anti-NR1s coupled to a solid support. The present disclosure also provides methods of treatment for anti-NMDAR pathology by the therapeutic anti-NMDAR antibody ART5803. The present disclosure also provides methods and systems for screening and predicting potential responsiveness to ART5803 therapy.

The invention discloses the technical field of pathology and space transcriptomics, and particularly relates to a gene expression prediction method and system based on multi-modal comparative learning and a guidance mechanism. Cutting the histological slice image into image blocks according to space coordinates; according to the method, a local convolution branch and a global Transform branch are combined to extract image features, the image features are mapped to a shared potential space through projection, soft contrast, hard contrast and global consistency constraints are introduced into the space, and cross-modal alignment of an image modal and a gene expression modal is realized; an expression prediction head is introduced in the training stage, representation learning is directly guided by a regression signal, and the relation between feature learning and gene expression prediction is broken through; in the inference stage, k-nearest neighbor retrieval and a multi-distance weighted aggregation strategy are combined to infer a gene expression profile of an unknown position. According to the method, the accuracy and robustness of space gene expression prediction can be effectively improved, the tissue space heterogeneity structure is kept, and the method has high clinical application and scientific research and popularization value.

The invention relates to the technical field of medical image diagnosis, and discloses a radiation-pathological diagnosis evaluation method based on weak supervision cross-modal deep fusion. The method comprises the following steps: receiving case-level radiation image data and pathological section data, combining with a weak supervision consistency label, realizing cross-modal semantic alignment through a double-branch feature extraction network, and generating aligned radiation feature vectors and pathological feature vectors; based on the aligned feature vector, a cross-modal attention fusion mechanism is adopted to complete deep fusion, and a fusion feature vector is obtained; a consistency evaluation task is executed based on a multi-task learning framework, and a consistency classification result, an inconsistency attribution result and a risk area positioning result are output; and based on the evaluation result, generating a visual diagnosis report through an interpretability analysis model. According to the method, cross-modal data can be effectively fused under a weak supervision condition, the accuracy and interpretability of diagnosis consistency evaluation are improved, and clinical data annotation requirements are met.

The invention relates to the technical field of medical detection and spectral analysis, and discloses a spectrum detection method for microorganisms on a wound surface based on knowledge data dual-drive envelope fusion. The method comprises the following steps: firstly, acquiring a multi-band near-infrared spectrum signal of a wound surface through a fiber optic spectrometer, and carrying out normalization and smooth filtering pretreatment; analyzing optical characteristic parameters based on a diffuse reflection theory model, constructing a knowledge-driven characteristic sample space, extracting spectral high-order characteristics by using a one-dimensional deep convolutional neural network, and constructing a data-driven characteristic sample space; then generating a grain envelope sample through a double-flow feature fusion mechanism; and finally, constructing a lightweight dual-task classification model based on the particle envelope sample, and realizing rapid detection of fungal infection of the wound surface and identification of seven common bacteria. According to the method, the interpretability, the recognition precision and the robustness of characteristic pathology are considered, and an efficient and reliable technical scheme is provided for early clinical diagnosis of wound infection.

Provided herein are compounds that promote targeted degradation of IKZF1, IKZF2, GSPT1, and / or CK1a, proteins whose activities are implicated in the pathology of certain cancers (e.g., acute myeloid leukemia). Also provided are pharmaceutical compositions comprising the compounds. Also provided are methods of treating cancer, and methods of promoting the degradation of IKZF1, IKZF2, GSPT1, and / or CK1a in a subject or biological sample by administering a compound or composition described herein.

An oncological foundation model is trained with broad, multimodal data to make predictions concerning a variety of different types of cancers. For example, the foundation model may make use of medical images drawn from radiology and pathology, as well as immunohistochemistry data; the presence or absence of biomarkers for particular diagnoses; patient history data; patient demographic data; and other forms of medical data. When using medical images, whole medical images as well as feature sets derived from the medical images may be used. The foundation model may have both causal predictive abilities as well as generative abilities.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention discloses a simulation image generation method, device and system and a storage medium. The simulation image generation method can comprise the following steps: acquiring a scanning image of a frozen section of the diseased tissue; processing the scanning image by using a pre-trained diffusion model to generate a simulation image of the paraffin section; wherein the diffusion model is embedded into a feature space mapping function to encode clinical feature information of the diseased tissue, and image features related to the scanned image are fused by using a multi-end attention mechanism. According to the method, the diffusion model is used for processing the frozen section image of the lesion tissue, and the realistic restoration of the paraffin section can be obtained, so that the accuracy of intraoperative frozen pathological diagnosis is improved.

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 invention discloses an artificial intelligence chest multi-organ three-dimensional reconstruction method, and belongs to the technical field of medical image processing. Comprising the following steps: acquiring a plurality of preprocessed target CT images, and performing AI organ recognition and labeling on each target CT image; performing chest multi-organ segmentation model training according to the marked CT image to obtain a segmentation model for performing organ segmentation on the marked CT image, and obtaining multi-layer cross section data of each chest organ for performing three-dimensional reconstruction of a single chest organ; acquiring relative position information of each chest organ, and combining with the three-dimensional reconstruction model of the single chest organ to complete three-dimensional reconstruction of multiple chest organs to obtain an initial three-dimensional model; and converting the initial three-dimensional model into discrete point cloud data, constructing a point cloud anomaly detection network model to perform anomaly recognition on the point cloud data, and analyzing an anomaly recognition result by using a pathology basis large model to obtain a pathology result for performing corresponding marking on the initial three-dimensional model to obtain a target three-dimensional reconstruction model.

In some aspects, a method, a system, or a non-transitory computer-readable storage medium are described for a foundation model for use in pathology, by providing an input dataset representing a plurality of pathology images as input to a backbone of the foundation model, wherein the plurality of pathology images comprises patches having different levels of pixel resolution; producing, with the backbone of the foundation model, a plurality of vector embeddings based on the input dataset; adjusting weights associated with the backbone of the foundation model based on the plurality of vector embeddings by using a Fourier reconstruction loss function configured to separate portions of the patches in accordance with a high-frequency band and a low-frequency band; and storing the foundation model on at least one storage device.

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 an angle-adjustable interventional biopsy needle suitable for deep tumors and a sampling method of the angle-adjustable interventional biopsy needle, and relates to the field of tumor interventional biopsy sampling, the angle-adjustable interventional biopsy needle comprises a guide sheathing canal, the guide sheathing canal is composed of an outer sheath layer on the outermost layer, a braid layer in the middle and a first lining layer on the inner layer, and a bending mechanism is arranged at the bottom of the guide sheathing canal; the bending mechanism comprises an active bending tube, the active bending tube is fixedly connected with the bottom of the guiding sheath tube, a protection pad is arranged at the bottom of the active bending tube, and a second lining layer is arranged on the inner side of the active bending tube. On the premise of single puncture, multi-point and multi-angle sampling of different quadrants and different depths in a tumor can be easily achieved, representative tissue samples can be obtained to the maximum extent through the design, sampling errors caused by tumor heterogeneity are effectively avoided, the occurrence rate of false negative results is remarkably reduced, and the sampling efficiency is improved. And a more reliable pathological basis is provided for clinical diagnosis.

The invention discloses a medical record diagnosis auxiliary system for thyroid puncture pathology and clinical data association analysis, and relates to the technical field of medical diagnosis intelligent analysis. Comprising a regional risk sensing module, a self-adaptive partition compression module, a transmission priority scheduling module, a detail integrity verification module, a cross-domain feature fusion and detail enhancement module and a diagnosis result return and model optimization module, in the digital scanning process, a regional risk perception model based on a pathological diagnosis task is established, a high-sensitivity region is calibrated according to cell nucleus density distribution, cell nucleus morphological anomaly degree and chromatin particle change, and corresponding lossless fidelity demand data is generated. Pathology key detail fidelity transmission and multi-dimensional feature fusion are achieved under the condition that resources are limited, the early-stage tiny focus recognition rate is increased, misdiagnosis and missed diagnosis are reduced, and diagnosis accuracy and efficiency are continuously improved through closed-loop optimization.

Techniques of automated quality control for digital pathology whole slide images are presented. The techniques include obtaining a thumbnail image derived from a whole slide image of a pathology slide; determining whether the whole slide image includes an artifact in a first class of artifacts by providing the thumbnail image to an electronic neural network trained to detect artifacts in the first class of artifacts by analyzing a plurality of labeled training thumbnail images; generating a tissue mask representing tissue depicted in the thumbnail image; determining whether the whole slide image includes an artifact in a second class of artifacts by performing a comparison using the tissue mask; and providing an indication of whether the whole slide image includes an artifact in the first class of artifacts or an artifact in the second class of artifacts.

Provided herein is a method for rapidly assessing a cattle patient for respiratory disease using targeted thoracic point-of-care ultrasound (TT-POCUS). This assessment begins with scanning a thoracic region of a lung of the cattle patient with an ultrasound probe to generate an ultrasound image. Subsequently, a count of A-lines and a count of B-lines are identified, along with the presence or absence of severe lung consolidation and / or abnormal pleural findings. The findings from the method can be used to distinguish interstitial pneumonia from non interstitial pneumonia, and predict future treatment success.

Systems and methods for a pathology review station are disclosed. The pathology review station may assist pathologists in analyzing, slicing, or sampling specimens by, in part, projecting an image onto a specimen. The process of projecting an image onto a specimen may include identification of the specimen based on characteristics of the specimen, characteristics of a tray supporting the specimen, or user input. The identified specimen may then be matched with an image obtained by an imager received at the pathology review station. A projected image may then be compiled based on the obtained image, which may include the entire image or features associated with the image. The provided systems and methods may assist a pathologist in, at least, removal of embedded surgical markers and / or determining where to take samples from a specimen.

A deep learning method, system, device, and medium for drug recommendation are disclosed. The method includes: establishing drug representations; establishing patient representations; and generating drug prediction results. These three steps first utilize an RNN to establish patient representations, then integrate patient diagnostic information, treatment information, and historical medication information, employing dual attention to assign weights at different element levels in patient history and single visits, and finally using a neural network for drug recommendation. Furthermore, patient similarity can be incorporated for drug recommendation, effectively improving the accuracy and safety of drug recommendations. The system, device, and medium, through the storage and utilization of relevant functional modules, realize drug recommendation using deep learning methods. As a medical auxiliary tool, it can significantly improve the discrimination quality and work efficiency of pathologists.

The invention discloses a method for constructing a special digestive disease large model based on two-stage pre-training, and the method comprises the steps: constructing a digestive tract pathological image data set containing different magnifications through multi-scale pathological image feature extraction, and extracting macroscopic and microscopic features through a multi-magnification segmentation algorithm. A visual converter architecture is adopted, first-stage pre-training is carried out based on multi-view mask self-supervised learning of a teacher-student architecture, and universal pathological features are learned. And in the second stage, a region-of-interest classifier is constructed through multi-instance learning, key regions are screened, a high-quality sample library is constructed, and comparative learning fine tuning is performed on the feature encoder to obtain an enhanced model. The model is used for digestive tract pathology downstream tasks, full-slice image level diagnostic analysis can be achieved through feature aggregation, and the accuracy and efficiency of digestive pathology diagnosis are improved.

1. The name of the design product: digital slide image scanner. 2. The use of the design product: the product is used for pathological diagnosis. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

Systems and methods are described for segmenting medical images, such as magnetic resonance images, using a deep learning model that has been trained using random dropped inputs, standardized inputs, or both. Medical images can be segmented based on anatomy, physiology, pathology, other properties or characteristics represented in the medical images, or combinations thereof. As one example, multi-contrast magnetic resonance images are input to the trained deep learning model in order to generate multiple segmented medical images, each representing a different segmentation class.

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.

The invention provides a short-needle-point rotary-cut type thick needle biopsy device which comprises a needle head structure, the needle head structure comprises an outer needle tube, a rotary-cut structure is rotatably connected in the outer needle tube, the rotary-cut structure comprises an inner needle tube, and the inner needle tube is rotatably arranged in the outer needle tube; an assembling structure is arranged at the top end of the needle head structure, a pneumatic structure is arranged in the assembling structure, the bottom end of the pneumatic structure is embedded into the inner needle tube, the pneumatic structure comprises a sealing cover, and an exhaust mechanism is fixedly installed in the middle of the sealing cover. By shortening the length of the tip of the needle core, the risk that the needle tip mistakenly enters adjacent important organs is remarkably reduced, a new sampling mechanism is adopted, displacement errors caused by forward pushing of the needle in traditional biopsy are avoided, uniform and continuous tissue cutting can be achieved, it is ensured that the obtained specimen is complete in shape, pathological diagnosis is facilitated, and the clinical application prospect is wide. Rapid sampling of tissues in areas difficult to operate and separate is ensured, so that puncture can be carried out in lesion areas closer to important structures, and the application range of the CNB technology is expanded.

The invention relates to a capsule type oxygen cabin for animal experiments, which adopts a modular structure, comprehensively considers the influence of multiple factors such as temperature, humidity, pressure and oxygen concentration, and also considers the influence of breathing oxygen consumption of experimental animals and the influence of a first / second accommodating part, and on the basis, the first / second accommodating part is centrally controlled through a touch screen; according to the method, the combination of multiple modes can be realized, so that the climate conditions of different regions can be accurately, efficiently and stably simulated, and accurate and scientific guidance can be provided for the research on'high reflection ', the work and life of personnel in high and cold climate regions and the pathology research of hospitals. Besides, the electrical parts are classified and placed in the first accommodating part, the second accommodating part, the touch screen and the bearing part, so that the cabin body can be transparent and visual, and the electrical parts in the first accommodating part and the second accommodating part are convenient to overhaul through detachable connection between the first partition plate and the second partition plate and the first accommodating part and between the first partition plate and the second partition plate and the second accommodating part.

The invention relates to the technical field of medical equipment, and discloses a slicing device for molecular pathology experiments. The cutting table is arranged on the base, and a cutter is arranged on the cutting table; the protective cover is arranged on the base, a first sliding groove is formed in the protective cover, a first sliding block is arranged in the first sliding groove in a sliding fit mode, a first bearing seat is arranged on the first sliding block, a main rod is rotationally matched with the first bearing seat, the first bearing seat does not limit the axial displacement of the main rod, a mounting head is arranged at one end of the main rod, and a wax block is mounted on the mounting head; the main driving mechanism is used for driving the first sliding block to slide back and forth in the first direction. The first driven mechanism is used for driving the main rod to rotate axially when the main rod moves along with the first sliding block; the second driven mechanism is used for driving the main rod to move in the second direction after the main rod reciprocates for one cycle in the first direction, low-loss and high-integrity cutting can be achieved, and the automatic and accurate feeding function is integrated.