321 results about "Pathological" patented technology

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention provides a patient pathological data analysis and evaluation method for clinical nursing, and relates to the technical field of nursing informatization. Comprising the steps of collecting biochemical and pathological data of a patient and standardizing units and reference values, analyzing the dynamic trend of detection values and classifying change directions and amplitudes, matching pathological labels and establishing association with nursing items, comparing differences between nursing records and the pathological data and establishing a transverse index relationship, and judging whether the nursing task difference exceeds a threshold value or not and adjusting a nursing path generation scheme. By integrating multi-source pathological data and standardizing units and reference intervals, data consistency and comparability are ensured, the index fluctuation trend is dynamically tracked, the change direction and amplitude are quantitatively analyzed, disease course evolution is accurately captured, direct mapping between pathological categories and nursing items is established, logic binding is clear, intervention precision is improved, and the method is suitable for clinical application. Differences between nursing records and pathological data are transversely compared, nursing dynamic adaptation is optimized, a path scheme is rapidly adjusted through threshold judgment, and the lagging or misjudgment risk is reduced.

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

The invention provides a medical examination data analysis system and method based on artificial intelligence. The method comprises the steps of obtaining a time sequence data matrix of multiple examination indexes of a target patient at different time nodes through medical examination data of the target patient; labeling pathological labels of various examination indexes in the medical examination data, and determining medical semantic association features among different examination indexes of the target patient through the pathological labels; according to the medical semantic association features and the time sequence data matrix, determining a time sequence association relationship of different inspection indexes of the target patient on a pathological level, and generating a fusion feature vector of the health state of the target patient according to the time sequence association relationship; and an abnormal evolution feature of the current health state of the target patient is obtained by combining an abnormal detection model with the fusion feature vector, and then an auxiliary analysis result of the pathological risk of the target patient is output to medical personnel. By adopting the scheme of the invention, the dynamic change trend analysis of the disease course risk state of the patient can be realized based on the medical semantic perception ability.

The invention relates to the technical field of medical big data analysis and intelligent monitoring, in particular to a medical time series data anomaly detection system which comprises a multi-modal data fusion module used for obtaining a physiological sensor data stream of a target object, performing multi-source heterogeneous synchronization and tensor coding on the physiological sensor data stream, and obtaining a multi-modal data fusion result; constructing a multi-modal physiological time sequence tensor; and the phase-space reconstruction module is used for performing high-dimensional dynamic mapping on the multi-modal physiological time sequence tensor. The one-dimensional time sequence signals are mapped to the high-dimensional Euclidean space through the phase-space reconstruction module, the dynamic manifold structure of the physiological system is restored, abnormity is recognized by detecting the morphological variation of attractor tracks in the high-dimensional space, and even if the physiological parameters do not reach the alarm threshold value in numerical value, the abnormity is recognized. As long as an internal nonlinear dynamic structure is changed, the system can carry out sensitive capture, so that the problem that a traditional system misses detection of early-stage hidden pathological features is effectively solved.

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

The invention, which relates to the technical field of medical health artificial intelligence, discloses a pathology critical value early warning management system based on a pathology knowledge graph, comprising a pathology data acquisition module, a pathology feature extraction module, a pathology state matching module, a pathology state early warning module and a pathology state management module. After pathological data are collected and preprocessed, a pathological feature extraction module constructs a knowledge graph and generates a composite feature vector, a pathological state matching module recognizes an acute pathological state and quantifies the matching degree, and a pathological state early warning module constructs a model in combination with an analysis result and outputs a pathological critical value. According to the method, intelligent early warning and management of the pathological critical value are realized by integrating the pathological knowledge graph and the artificial intelligence technology, the accuracy and efficiency of pathological analysis are improved, visual early warning information and decision support are provided for clinicians, the acute pathological state can be found and processed in time, and the treatment effect of a patient is improved.

The invention provides an auxiliary pathological analysis method for a microscope system, and belongs to the technical field of pathological analysis, and the method comprises the steps: firstly carrying out the gray normalization, denoising and other preprocessing of an obtained pathological section image, and carrying out the illumination equalization processing through a region adaptive histogram equalization algorithm; then, color features of the image are extracted in an LAB color space, a color representation center is calculated, a feature mapping matrix is established, and meanwhile texture features are extracted and fused with the color features; and carrying out dimension reduction on the fused feature vector by using a principal component analysis algorithm, and reserving important feature information. And then, establishing a convolutional neural network model, inputting the feature vectors after dimension reduction, performing training and cross validation by using a labeling data set, and improving the model performance through iterative optimization. According to the invention, the technical problem that the diagnosis and judgment of a doctor are seriously influenced due to inconsistent color expression caused by the difference of shooting conditions of pathological section images observed by a microscope is solved.

The invention relates to the field of man-machine interaction, and particularly discloses a multi-mode man-machine interaction application system for sky-eye insight based on a pathologist visual angle, which is characterized in that firstly, a candidate focus thermodynamic diagram is generated through rapid scanning of a full-slice image, low-power lens global browsing of a doctor is simulated, and the doctor is guided to lock a key area interactively through professional judgment; therefore, the processing efficiency of the oversized image is greatly improved. And then, the system only performs high-resolution deep analysis on the focus confirmed by the doctor, and performs multi-modal fusion on the extracted microscopic visual features and the patient text information to generate a preliminary report with an interpretable basis, so that the problem that the multi-modal function deviates from a clinical core task is solved. Finally, the doctor can check and finalize the report through visual interaction, and the dominant position and the final decision making right of the doctor in the diagnosis process are ensured, so that the bottlenecks of black box operation and low clinical acceptability of a traditional AI system are overcome.

The invention discloses a medical question-answering method, device, equipment and program product, and is applied to the technical field of medical question-answering. The method comprises the following steps: acquiring a disease image and disease chief complaint information; performing feature extraction on the disease image and the disease chief complaint information to obtain pathological visual features and chief complaint text features; performing feature fusion on the pathological visual features and the chief complaint text features to obtain target fusion features; determining a dynamic cue word template based on the information type of the target fusion feature; and inputting the dynamic cue word template and the disease image into a preset large language model, and generating a medical question and answer result by the large language model. According to the method, a complete, dynamic and adaptive cue word template is generated according to the consistency or conflict fuzziness embodied by the multi-modal information, so that the dynamic cue word template can further put forward a more complex task with question and answer guidance to a large language model, the pertinence and depth of medical question and answer are improved, and the medical question and answer efficiency is improved. And the precision of medical questions and answers is effectively improved.

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 invention discloses a multi-modal information fusion method, system and device for a large language model oriented to pathological full slices, and a medium. The obtained coding vector of the local representation of the pathological image region block is input into a pathological image information mixed attention layer to be fused to obtain a representation vector of a full slice of the pathological image; performing multi-modal fusion on the obtained representation vector of the full slice of the pathological image and the text vector of the large language model through a cross attention mechanism; the system, the equipment and the medium are based on the multi-modal information fusion method of the pathological full-slice-oriented language large model, and multi-modal information fusion is carried out according to pathological image information and text information input by a user; according to the invention, doctors are helped to carry out quicker and more accurate pathological judgment.

The invention discloses a classification method and a classification device for pancreatic neuroendocrine tumors. The classification method comprises the following steps: using a pre-trained segmentation model to segment a plurality of HE full slice images from a plurality of patients; quantizing each segmented HE full slice image to determine a plurality of first feature parameters; based on the cellular morphology and topological features of each HE full-slice image, extracting a plurality of second feature parameters from each HE full-slice image; for each HE full slice image, screening out a target feature from the plurality of first feature parameters and the plurality of second feature parameters, and calculating a pathological omics score; inputting the pathological omics scores and the conventional pathological features of the plurality of HE full slice images into a logistic regression model for training to obtain a trained logistic regression model; and classifying the HE full slice images to be classified by using the logistic regression model. According to the method, pancreatic neuroendocrine tumor G classification can be quickly and accurately realized at low cost.

The invention relates to a multi-modal survival prediction method fusing a digital pathological image and omics data. The method comprises the following steps: acquiring a full-view digital pathological image and omics data of an esophageal squamous cell carcinoma patient; carrying out preprocessing and feature extraction on the full-view digital pathological image and the omics data to respectively obtain a pathological embedding representation and an omics embedding representation; fusing the pathological embedding representation and the omics embedding representation by using a multi-head attention mechanism of Transform to obtain a preliminary fusion feature; performing deep interaction on the pathological embedding representation and the preliminary fusion feature to obtain a final fusion feature; and predicting a survival result based on the final fusion feature. According to the method, bimodal sharing of pathological images and omics data and full fusion of features are realized, and prognosis survival prediction of esophageal squamous cell carcinoma cancer patients can be better realized.

The invention discloses a lung disease multi-subtype classification method, system and device based on chest images, and belongs to classification of lung diseases in the technical field of artificial intelligence medicine. The method aims at solving the technical problem that in the prior art, due to the fact that a loss function cannot enable a model to pay attention to main pathology types and all pathology types in data at the same time, the multi-subtype classification accuracy of the model is low, the multi-subtype classification model is constructed, and three different losses are creatively synthesized during model training, so that the multi-subtype classification accuracy of the model is improved. The # imgabs0 # is used for enabling the model to predict all existing pathological types, the # imgabs1 # is used for enabling the model to mainly learn to predict the main pathological types, and the # imgabs2 # is used for enabling the model to restrain prototype vectors of various types to have distinction degrees, so that the model can better capture correlation among different subtypes while learning image representation of main pathological subtypes, and the model can be used for predicting the main pathological types. The generalization ability of the model is improved, the robustness of model training is improved, the overfitting problem of the model is relieved, and the accuracy of the model for multi-subtype classification is improved.

The invention provides a method and device for performing structured integration on a pathological report, and the method comprises the following steps: obtaining multi-modal medical data, and standardizing each medical term in the medical data to obtain standardized data; labeling the standardized data in a layered labeling manner to obtain labeled data, and performing entity and entity relationship extraction on the labeled data by using a pre-trained multi-dimensional relationship extraction network to obtain entity relationship data; and performing knowledge graph construction on the entity relationship data in entity dimension, time dimension, space dimension and evidence dimension to obtain a structured pathological report. According to the scheme, through automatic structured processing of the pathological data, key information can be rapidly extracted from massive pathological reports, a high-quality data basis is provided for subsequent data analysis and application, and the accuracy and efficiency of medical research and clinical decision making are improved.

A method and apparatus are proposed for training a model for predicting information representative of a pathology progression at a future time (t+Δt) from one or more examination images (for instance one image 2D + a 3D volume) of a region of interest of a patient captured at a current time (t), said method comprising: - getting (20) a training dataset, comprising time series of examination images of said region of interest from patients associated with labels, said labels (S(t)) comprising said information representative of a pathology progression, - training (E1, 21, 22) the machine learning model (MLM) with inputs from said training dataset comprising, at least one of said inputs comprising a pair of consecutive examination images comprising a first image (x(ti)) associated with a first time (ti) and first label (S(ti)), a second image (x(ti+1)) associated with a second time (ti+1) and a second label (S(ti+1)), to output a target intermediate label (TS(tmix)) at an intermediate time selected within a time interval ([ti,ti+1]) between the first and second examination images, said target intermediate label (TS(tmix)) being determined for said intermediate time (tmix), based on a given pathology progression profile (I(t)).

The invention relates to the technical field of medical information, in particular to a multi-source data health intervention method, which comprises the following steps: firstly, acquiring texts, vital signs, image vectors and wearable signals, generating a unified vector through multi-modal coding, and extracting a topological life vector; constructing a spiking neuro-causal diagram based on a unified vector, and injecting a life vector as an external field into a Sheng differential equation to generate a dynamic pathological manifold; compressing the manifold into a tensor network state, constructing a quantum optimization model in combination with a causal diagram, minimizing energy expectation and topological risk to obtain an optimal intervention sequence, and reinjecting a target gradient to adjust the manifold in real time; after the sequence is subjected to clinical logic verification, a commitment value and a zero-knowledge proof are generated and written into a Byzantine account book, and meanwhile a control instruction is issued to an execution terminal. According to the method, millisecond-level decision making is achieved, the false alarm rate is reduced, and chain traceability is provided.

The invention discloses a disease hidden danger intelligent analysis and processing system and method based on epidemic disease big data, and belongs to the technical field of epidemic disease big data. Establishing a climate disease archive library to store climate disease medical records; on the basis of the pathology initial stage symptom generation time data cluster and the pathology late stage symptom generation time data cluster, locking climatic disease medical records, and tracing and capturing disease types and meteorological induction factors to generate feature driving labels; respectively and sequentially mapping the pathological initial-stage sample set and the pathological late-stage sample set into a pathological initial-stage feature matrix and a pathological late-stage feature matrix to respectively obtain a pathological initial-stage feature matrix and a pathological late-stage feature matrix with time node marks; analyzing a time correlation coefficient between the initial pathology stage and the late pathology stage, and evaluating whether a disease hidden danger relationship exists between the initial pathology stage and the late pathology stage; furthermore, the correlation between the early blight and the late blight can be monitored for a long time, and time prediction assistance is provided for the hidden danger of the late blight.

The invention relates to the technical field of disease diagnosis, in particular to a digital pathological section diagnosis method based on a link algorithm, and the method comprises the following steps: creating a tissue; creating a link algorithm; marking the digital pathological section data; checking the digital pathological section data; corresponding parameters are set for a link algorithm, and training is carried out; if the training effect of the link algorithm meets the requirement, cascading a new module, and if the training effect of the link algorithm does not meet the requirement, continuing to enrich the data set until the requirement is met; and after the whole link algorithm is trained, exporting a trained model, and reasoning the digital pathological section by using the model. According to the method, a link algorithm is created, so that operators influencing the reasoning speed and memory occupation are removed while the precision is kept; by creating a link algorithm, a user can train a data set in a highly self-defined manner in combination with various downstream tasks so as to generate a model meeting own requirements.

The invention discloses a double-domain RAG-driven multi-omics fusion pathology analysis system, and belongs to the technical field of artificial intelligence of medical data. Pathology image feature data and structured multi-omics data of a patient are fused in a semantic layer through a multi-modal fusion module, a semantic layer fusion result is obtained, and a comprehensive representation vector of the patient is generated; the double-domain retrieval module obtains internal reference evidence corresponding to a hospital case knowledge base and external reference evidence corresponding to an external medical literature knowledge base; the consistency gating fusion module analyzes the consistency of the internal reference evidence and the external reference evidence, and fuses the internal reference evidence and the external reference evidence to obtain a fused credible evidence; and the report generation module generates a medical auxiliary report with an evidence chain based on a large language model according to the semantic layer fusion result and the credible evidence. According to the embodiment of the invention, the interpretability and credibility of the diagnosis conclusion can be enhanced.

The invention aims to provide a multi-modal cancer prognosis evaluation method based on Transform, which comprises the following steps: S1, pre-processing multi-modal data, carrying out foreground segmentation and patch segmentation on pathology to obtain patch features, and clustering to obtain a phenotype cluster expressed by morphological specificity; performing expression significance analysis on the multi-omics data, and screening significant expression features; s2, single-mode modeling is carried out, strong pathological representation is obtained based on Transform and a two-stage attention convergence layer, and multi-omics modeling is realized based on a self-normalization network (SNN); s3, cross-modal interaction is carried out, and dense omics-phenotype cluster common attention mapping is realized by using a cross-modal Transform; s4, performing multi-modal fusion, and integrating the cross-modal interaction feature, each single-modal feature and clinical information by using a unified Transform coding layer to obtain a final multi-modal representation; and S5, risk prediction: inputting the final multi-modal representation into a risk prediction module to obtain a risk prediction result. Pathology and multi-omics characteristics are effectively fused, and clinical decision making is promoted.

The invention provides a pathological section human-like reading track generation method based on reinforcement learning. The method comprises the following steps: constructing a training data set; the training data set comprises a plurality of WSIs and corresponding doctor film reading track data; an RL frame is built, and parameters of the built RL frame are initialized; wSI local image features and a WSI current film reading state are taken as a state S, position movement in eight directions and a preset fixed step length is taken as an action A, and a pathological expectation value output by a PEAN model is taken as a reward R; and training a PEAN model agent based on the deep reinforcement learning Q network and a sequence of the state S, the action A, the reward R and the next state S stored in the experience playback pool to realize iterative optimization of the deep reinforcement learning Q network so as to finally generate a human-like film reading track of which the coincidence degree with the doctor film reading track is greater than or equal to a preset coincidence degree. According to the method, the macroscopic and microcosmic film reading logic of a doctor is reproduced, and the WSI diagnosis efficiency is greatly improved.

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 image segmentation, in particular to a multi-histological pathological cell nucleus instance segmentation and classification method and system based on a key point guided Transform, and the method comprises the steps: inputting a preprocessed pathological image into a PointFormer, and generating three output predictions, namely a key point heat map, a nucleus prediction and an edge prediction; positioning a central point of a cell nucleus by filtering local peak values in the key point heat map, and generating a central point map; integrating the kernel prediction and the edge prediction to obtain a class-independent segmentation prediction mask; and mapping the category of the corresponding central point in the central point diagram to a category-independent segmentation prediction mask, and generating a final cell nucleus instance segmentation classification mask. According to the method, detection and classification of each kernel are used as a semantic key point estimation problem to determine the center point of each kernel, and benefits can be obtained from category-perceived detection and category-independent segmentation by decoupling a segmentation classification task and utilizing the advantage that Transform has modeling long-distance dependence, so that the segmentation classification performance of aggregated and overlapped kernels is improved.

The invention discloses an intraocular light field simulation method and system based on physiological constraint and pathology traceability, the system takes a Physics Informed Kolmogorov-Arnold Net (PI-KAN) network as a core, namely physical information KAN, the method comprises the following steps: obtaining personalized parameters of eyeballs; 5-dimensional light field parameters including space, wavelength and time are input into a pre-trained PI-KAN model for light field solving, a three-layer network architecture including an input layer, a hidden layer and an output layer is established, a physical information edge function is constructed, and training is performed through fusion of a Helmholtz equation and a loss function of boundary conditions; generating an OCT image based on the light field solved by simulation; by analyzing side function mapping, visualization and pathological traceability of a light field propagation physical mechanism are realized. According to the method, the sparsity and interpretability of PI-KAN are utilized, the problems that a traditional method is low in calculation efficiency, difficult in high-dimensional modeling, weak in physical constraint and poor in interpretability are solved, millisecond-level, high-precision and interpretable simulation of the eye light field is achieved, and the method is suitable for ophthalmic clinical auxiliary diagnosis, surgical planning and equipment optimization.

The invention discloses an intelligent insurance underwriting method. Insurance requirements, health notification information and multi-mode physical examination data of a user are obtained through a dialogue interaction interface; extracting text physiological indexes by adopting a named entity recognition technology, analyzing medical images through a visual language model to generate pathological descriptions, and mapping the pathological descriptions into standard medical term codes; performing preliminary screening based on a decision tree rule engine, and calculating a deviation degree of health indexes and terms in combination with a disease correlation model to screen candidate products; constructing a hierarchical cue word template to drive the large language model to execute clause semantic analysis, and outputting a structured underwriting conclusion; and generating a product recommendation sequence by using a weighted scoring model, or triggering a difference comparison description. Through multi-modal data fusion, dynamic deviation calculation and an anti-factual reasoning mechanism, the problems that unstructured data is difficult to process and clause matching is rigid in traditional underwriting are solved, the underwriting accuracy is effectively improved, the decision-making efficiency is remarkably improved, real-time synchronous updating of supervision clauses is supported, and the compliance risk is remarkably reduced.

The invention discloses a pathology grading method and device, a storage medium and a computer program product, and relates to the technical field of medical image processing.The pathology grading method comprises the steps that a light microscope image of renal biopsy is obtained, a preset glomerulus recognition model is used for detecting the light microscope image, and slice images of all glomerulus are obtained; according to the slice image, determining a proportion result of each type of glomerulus in the light microscope image; and determining a pathology grading result according to the proportion result and a preset pathology grading rule. According to the method and the device, the glomerulus in the biopsy image is automatically identified and classified, and automatic classification is carried out according to the clinical pathology classification standard, so that the workload of a clinician in pathology classification is reduced, and the accuracy and the efficiency of pathology classification are improved.

The invention discloses a cardiovascular risk early warning method based on multi-modal time sequence data, relates to the technical field of medical health information monitoring, and aims to solve the problem of confusion of causes of dyspnea at night by constructing a cross-modal direction and time delay relation in a sliding time window. Phenotype similar phenomena such as pure blood oxygen reduction / wake-up are decomposed into a comparable time sequence interaction structure, so that a blocking chain type process and a non-blocking type heart failure related process can be distinguished on the structural level, and therefore false alarm and missing alarm caused by confusion are reduced. Meanwhile, a multi-channel signal is firstly converted into a time sequence causal diagram, and then in-window statistical characteristics are combined for judgment, so that the model not only utilizes the self change of each channel, but also utilizes the interaction evidence of the first and second channels, the influence direction and the delay length, and the expression ability of the pathophysiological chain difference is improved.

The invention discloses an intelligent screening method and system for pathological diagnosis quality control, and belongs to the technical field of artificial intelligence, and the method comprises the steps: receiving a to-be-recognized pathological section image and a corresponding diagnosis report issued by a doctor; processing the to-be-identified pathological section image according to a pre-constructed auxiliary diagnosis model and generating a prediction report; and when the prediction report is inconsistent with the diagnosis report, outputting a screening result for rechecking. According to the method, possible diagnosis errors can be quickly and automatically identified, the risk of missed diagnosis and misdiagnosis can be reduced, the proportion of manual recheck can be effectively reduced, the workload of a qualification pathology expert is relieved, the working efficiency is effectively improved, and the qualification pathology expert can concentrate on diagnosis and treatment of difficult cases.