56 results about "Lesion types" patented technology

A non-invasive tool for skin disease diagnosis would be a useful clinical adjunct. The purpose of this study was to determine whether visible / near-infrared spectroscopy can be used to non-invasively characterize skin diseases. In-vivo visible- and near-infrared spectra (400-2500 nm) of skin neoplasms (actinic keratoses, basal cell carcinomata, banal common acquired melanocytic nevi, dysplastic melanocytic nevi, actinic lentigines and seborrheic keratoses) were collected by placing a fiber optic probe on the skin. Paired t-tests, repeated measures analysis of variance and linear discriminant analysis were used to determine whether significant spectral differences existed and whether spectra could be classified according to lesion type. Paired t-tests showed significant differences (p<0.05) between normal skin and skin lesions in several areas of the visible / near-infrared spectrum. In addition, significant differences were found between the lesion groups by analysis of variance. Linear discriminant analysis classified spectra from benign lesions compared to pre-malignant or malignant lesions with high accuracy. Visible / near-infrared spectroscopy is a promising non-invasive technique for the screening of skin diseases.

The invention provides a lung image retrieving method and apparatus. The method comprises the following steps: a type of a lesion contained in a to-be-retrieved lung image is obtained and a corresponding retrieving feature vector is obtained from the to-be-retrieved lung image based on the lesion type; and in a sample image library corresponding to the lesion type, at least one sample lung image with the highest similarity with the to-be-retrieved lung image is obtained based on the retrieving feature vector, wherein the sample image library includes various sample lung images belonging to thesame lesion type and corresponding sample feature vectors obtained from all sample lung images based on the same lesion type. According to the method provided by the invention, corresponding retrieving feature vectors are obtained based on different lesion types in the to-be-retrieved lung image and similar sample lung image retrieving is carried out based on the retrieving feature vector, so that the lung image retrieval accuracy is improved.

The invention provides a computer-assisted recognition device and a corresponding computer-assisted recognition system. The computer-assisted recognition device comprises a scanning data acquiring module, a scanning image generating module, an acupoint information acquiring module and a lesion type recognizing module, wherein the scanning data acquiring module is used for receiving a scanning signal of a lesion region of a patient and acquiring the scanning data of the lesion region according to the scanning signal; the scanning image generating module is used for generating a scanning image of the lesion region according to the scanning data; the acupoint information acquiring module is used for receiving detection signals of at least one acupoint of the patient and acquiring the acupoint information of at least one acupoint according to the detection signals; and the lesion type recognizing module is used for recognizing the lesion type of the lesion region according to the image information in the scanning image and the acupoint information of at least one acupoint. Therefore, the research results of the meridian theory in the traditional Chinese medicine field can be effectively applied to the modern medical imaging technology to improve the probability of accurate diagnosis of lesions.

The invention relates to a robotic laser beautifying and treatment system. The robotic laser beautifying and treatment system comprises an intelligent vision system, a man-machine interaction system and a tail end laser execution system, wherein the intelligent vision system is used for acquiring and processing skin images; the man-machine interaction system is used for operating the images by a user; and the tail end laser execution system is used for emitting laser in a designated area according to operation of the user and image processing results. The robotic laser beautifying and treatment system can provide an optimal treatment strategy, including laser parameters, irradiation time, and the like according to a skin type and lesion type given by a doctor through combination of a skinsurface vision technique by using an artificial intelligence method. According to the treatment strategy, the laser parameters are accurately controlled by adopting the mechanical operation, so that the influence of the laser on non-focus points is reduced; and the positioning accuracy of a focus point is improved by utilizing the robot-assisted treatment mode, and the working efficiency of doctors is improved.

The invention discloses a thyroid cytology multi-type cell detection method based on deep learning, and the method comprises the steps: obtaining a cell pathology slide image of a thyroid cell, obtaining a plurality of target images from the cell pathology slide image through sliding window scanning with selectable step length, the adjacent target images have an overlapping area, the target images are processed to obtain at least one feature image and confidence images, and the number of the confidence images is the same as the number of lesion types of lesion cells of the thyroid; and processing the at least one confidence coefficient image to obtain a plurality of confidence coefficients corresponding to different lesion types, obtaining a classification result matched with the target image based on the plurality of confidence coefficients, and processing the at least one confidence coefficient image based on the classification result to obtain an area matched with the classification result in the target image. Therefore, the detection efficiency of the detection system can be improved, and the marking cost can be reduced when the detection system is trained.

The invention discloses a chest X-ray disease detection device and method based on a dual-channel separation network, and the device comprises a processor which is configured to carry out the following operations: carrying out the preprocessing of a training data set of a chest X-ray image, and dividing the training data set into a data enhancement part and a normalization regularization part; training a dual-channel separation depth network, extracting and fusing features of each level in different channels, minimizing a loss function in a classification layer, and performing complete training of the network; classifying the input chest X-ray images by using the trained network to obtain lesion types and probabilities contained in the images; and using the trained network to locate the disease in the input chest X-ray image. The method can greatly improve the recognition accuracy of chest lesions in a chest disease recognition task, and can achieve the precise positioning of a diseaseposition in a visualization task.

The invention discloses a breast rheid model applied to mammary gland impedance scanning detection technique training, and a preparation method of the model. The model mainly consists of a conductive rubber film layer, sepharose gel and an electrolyte solution, wherein the sepharose gel and the electrolyte solution are wrapped by the conductive rubber film layer in a sealed manner so as to form an overall hemispherical breast rheid model. The model is capable of vividly simulating form and impedance distribution state of a breast and has the characteristics of high form simulation degree, approximation to real impedance parameters, simplicity in preparation process and the like, different model phantoms can be prepared according to structural types and lesion types of the breast, and meanwhile the model can be repeatedly used for a long time and is beneficial for long-term operation training.

The invention proposes an artificial intelligence diagnosis and classification system for lumbar disc herniation. The system includes a data collection module, a marking module, a neural network classifier and a judgment module, wherein the data collection module receives several lumbar images, the marking module is connected with the data collection module to generate a lesion marker for each lumbar image and associate the lesion markers with the corresponding lumbar images for storage, the neural network classifier is trained by using each lumbar image and the corresponding lesion marker, and the judgment module receives a to-be-judged lumbar image and uses the neural network classifier to distinguish the lesion type of the to-be-judged lumbar image. The disclosed diagnosis and classification system can automatically judge whether or not the lumbar intervertebral disc is protruding and the degree of protruding, and meanwhile perform Pfirrmann classification and MSU classification, avaluable reference basis is provided for selection of a disease treatment scheme, and a basis is provided for taking prevention measures to prevent or slow down the occurrence of diseases.

The invention provides an AMD grading system based on a macular attention mechanism and uncertainty, and relates to the technical field of macular disease detection. An optic disc segmentation image and a blood vessel segmentation image are obtained by using a segmentation network model, a macular area image is obtained according to the obtained optic disc segmentation image and the blood vessel segmentation image, a multichannel image is obtained by using an attention network sub-module, features are extracted by using a Bayesian deep learning classification network model, and through multiple times of dropout Monte Carlo, four groups of probability values and one group of noise corresponding to the four lesion types are output; and the classification network module gives out accidental uncertainty and model uncertainty while finally outputting a model classification result. And the safety performance of the model is ensured.

The invention relates to the field of image recognition of computer vision and the field of artificial intelligence, and provides a common colon disease classification method based on a deep neural network and an auxiliary system, and the auxiliary system comprises a model training module and an auxiliary diagnosis module. The method comprises the steps of obtaining a multi-classification data setand a binary classification data set; training based on the binary classification data set to obtain a normal abnormal deep neural network classification model; training based on the multi-classification data set to obtain a lesion type deep neural network classification model; fusing the two models to obtain a deep neural network disease classification model of the colonoscope image, in the model, firstly inputting the image in the test set into a normal abnormal deep neural network classification model, if the image is judged to be normal, directly outputting a judgment result, and if the image is judged to be abnormal, inputting into a lesion type deep neural network classification model, further determining the lesion type, and outputting a judgment result.

The invention provides a low-frequency audible sound wave traditional Chinese medicine pulse diagnosis instrument. The low-frequency audible sound wave traditional Chinese medicine pulse diagnosis instrument includes: a sound wave extraction device for acquiring audible sound wave information of cun-guan-chi three different positions of a radial artery of a human body; a pressure applying device for acquiring sound wave information of the cun-guan-chi three positions at different laminar flow densities; a database for pre-storing disease types corresponding to sound waveforms at cun-guan-chi different positions and different laminar flow densities; a processor configured to receive and analyze audible sound wave information at a predetermined frequency band and extracted by the sound waveextraction device, extract a sound waveform closest to the received sound wave data in the database, and determining a disease type corresponding to the waveform. The beneficial effects of the invention are that the instrument can accurately detect audible sound wave type obscure pulse in pulse observation, can accurately analyze pathological lesion types of human organs corresponding to the audible sound waveforms of different obscure pulses by analyzing the obscure pulses, can greatly improve the pulse diagnosis accuracy of the micro sphygmology, and can provides a decision basis for traditional Chinese medicine pulse diagnosis.

The invention relates to the technical field of medical image analysis, in particular to a lung pathological image classification and segmentation method. The method comprises the following steps: inputting a pathological image of a full slice; segmenting the pathological image by using a sliding window to obtain image blocks; analyzing foreground image blocks in sequence by using a focus type classification model, and identifying focus types of tissue regions in the foreground image blocks; outputting a focus type classification result; and splicing the foreground image block lesion region segmentation results according to the relative positions of the corresponding foreground image blocks in the pathological image, and filling a background image block region with the background to obtainpathological image lesion region segmentation results. According to the invention, the specific boundary of the lesion area is segmented while accurate lesion type identification is carried out on the lung pathological image.

The invention discloses an eye fundus image recognition method and device, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence such as computer vision, deep learning and intelligent medical treatment. A specific embodiment of the method comprises the following steps: acquiring a position of a fundus focus, a position of a macular fovea center, a lesion level of a retina and a lesion probability value of a macular region in a fundus image; establishing the correlation between each feature and the lesion type of the macular region based on the position of the fundus focus, the position of the fovea center of the macular, the lesion level of the retina and the lesion probability value of the macular region; performing feature screening based on the correlation between each feature and the lesion type of the macular region; and inputting the screened features into a pre-trained macular region classification decision tree to obtain the category of the macular region. According to the embodiment, computer-assisted eye fundus image recognition is utilized, so that the labor cost is greatly reduced.

Repetitive electrical activity produces microstructural alteration in myelinated axons. These transient microstructural changes can be non-invasively visualized via two different magnetic-resonance-based approaches: diffusion fMRI and dynamic T2 spectroscopy in the ex vivo perfused bullfrog sciatic nerves. Non-invasive diffusion fMRI, based on standard diffusion tensor imaging (DTI), clearly localized the sites of axonal conduction blockage as might be encountered in neurotrauma or other lesion types. Diffusion fMRI response was graded in proportion to the total number of electrical impulses carried through a given locus. Diffusion basis spectrum imaging (DBSI) method revealed a reversible shift of tissue water into a restricted isotropic diffusion signal component, consistent with sub-myelinic vacuole formation.

The invention provides a fundus lesion recognition method and device. The method comprises the following steps: acquiring an OCT fundus image of a specific patient; determining boundary information of a retina tissue of the OCT eye fundus image by adopting a first neural network model, and segmenting the OCT eye fundus image into a plurality of image blocks according to the boundary information of the retina tissue; the plurality of image blocks are provided for a second neural network model to obtain the fundus lesion type of the specific patient, and the second neural network model calculates the feature vector of each image block and the attention weight of each image block for the set fundus lesion type; and performing weighted calculation to obtain a fused feature vector so as to obtain an identification result aiming at a set fundus lesion type. Wherein the second neural network model reinforces important information in the image block group in combination with the spatial domain significance and the scanning position significance, and filters unimportant information, so that low-cost model training and application are realized.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence system for identifying high myopia retinopathy, comprising: an image acquisition module for acquiring an optical coherence tomography image and a fundus reference of a patient to be identified; the first recognition module is used for inputting the optical coherence tomography image into a high myopia retinopathy recognition model, judging whether the optical coherence tomography image has lesion or not and obtaining a lesion type result; the second recognition module is used for inputting the fundus color photograph into a high myopia retinal lesion staging model, judging the retinal lesion staging of the fundus color photograph and obtaining a lesion staging judgment result; and the report generation module is used for generating a diagnosis and treatment suggestion report according to the lesion type result and the lesion staging judgment result. According to the method, the optical coherence tomography image and the fundus color photograph of the patient can be quickly and effectively recognized to recognize common retinopathy of high myopia.

The invention discloses an eyelid tumor digital pathological section image multi-classification method based on deep learning. The method comprises steps of scanning eyelid tumor pathological sections classified by known lesion categories to obtain an image construction training set; performing data enhancement and normalization processing, constructing a three-layer cascaded tumor digital pathological section diagnosis network, training the tumor digital pathological section diagnosis network by using an enhanced training set, performing prediction processing, generating a probability thermodynamic diagram, and performing lesion category detection. The method can effectively visualize the position and lesion type of the tumor in the full-field digital slice to assist in diagnosis, perform preliminary screening and prompting of lesion areas, change the attention of the network to channels and improve the network performance.

The invention discloses a digestive tract focus auxiliary identification and positive feedback system based on a cloud platform. The digestive tract focus auxiliary identification and positive feedback system comprises a data collection device, a cloud platform, an online error correction module and an online optimization module, wherein the data collection device is configured to collect a to-be-identified digestive tract image and transmit the to-be-identified digestive tract image to the cloud platform; the cloud platform is configured to establish an inference model, construct a training set to perform optimization training on the inference model, utilize the inference model to carry out alimentary canal position and lesion type inference on the received image, and respectively store the image and digestive tract focus identification results; the online error correction module is configured to check an inference result online and mark and correct the wrongly inferred result for a doctor client; and the online optimization module is configured to add a corrected image into the training set and train the inference model again. According to the digestive tract focus auxiliary identification and positive feedback system, the accuracy of digestive tract focus identification can be improved, and the detection rate of digestive system diseases is improved.

The invention discloses an automatic interpretation system for a cell pathology smear. The system comprises an imaging module; an image acquisition module; an image storage and management module; an image preprocessing module; the intelligent interpretation module receives the image output by the image preprocessing module, predicts and classifies a normal sample and a lesion sample for the image, and further predicts and interprets the lesion type when the image is the lesion sample; and the report writing module automatically generates an interpretation conclusion text of the sample corresponding to the image. According to the method, the artificial intelligence auxiliary diagnosis technology is successfully applied to the rapid staining cytopathology, the accuracy and consistency of diagnosis can be remarkably improved, and the workload of cytopathology doctors is reduced; according to the method, the existing convolutional neural network is improved, the multi-channel attention mechanism characteristics are utilized, uncertain factors introduced by manual sampling are solved, high-accuracy full-scene and multi-classification tasks can be achieved, and finally the accuracy of classification interpretation results can be improved.

The invention provides a lesion classification method and device, and a training method of a lesion classification model. The lesion classification method comprises the steps that a first lesion areain a first medical image and a second lesion area in a second medical image are acquired, wherein the first medical image and the second medical image are images obtained by shooting an examination part of the same examination object at different angles, and the first lesion area and the second lesion area are image areas corresponding to the same lesion in the first medical image and the second medical image respectively; feature extraction is respectively performed on the first lesion area and the second lesion area; the features of the first lesion area and the features of the second lesionarea are fused to obtain a first fused feature of the lesion; and according to the first fusion feature, the lesions are classified. Therefore, the resolution capability of the lesion types can be improved, and the lesion classification accuracy is improved.

The invention provides a premature infant retinopathy plus lesion classification system based on uncertainty, and relates to the technical field of deep learning. The method comprises the following steps: via a vascular segmentation module, firstly, segmenting a blood vessel of a fundus image; using a Bayesian deep learning classification network model to extract features via a classification module; performing dropout Monte Carlo for multiple times; obtaining three groups of probability values corresponding to three lesion types and a group of image noise, calculating the mean value and variance of each group of probability values, taking the lesion type with the maximum mean value as a final classification result, and taking the mean value of the image noise as accidental uncertainty andvariance sum as model uncertainty. When the method is actually put into use, the credibility of the image classification result can be judged through two kinds of uncertainty instead of selecting a diagnosis result given by a blind belief model; for doctors and patients, whether artificial ophthalmology experts are needed for re-diagnosis or not is considered to be very helpful, and the method issafer and more reliable in actual clinical use.

The embodiment of the invention provides a mammary gland X-ray image analysis method and device based on joint symptoms. The method comprises the steps that a focus area is acquired; extracting focusarea image features of the mammary gland X-ray image based on a pre-trained feature extraction model; inputting the lesion area image features into a lesion type graph model to obtain a lesion type representation vector; feature transformation processing is performed according to the focus area image features in combination with the focus type obtained by the focus type graph model so as to obtainthe determined type of the focus; inputting the focus area image features into a specific symptom graph model corresponding to the determined type, the specific symptom graph model comprising a plurality of symptom nodes, and at least two symptom nodes in the plurality of symptom nodes being connected through a connecting line used for representing relevancy; and obtaining image symptom featuresoutput by the specific symptom graph model.

The invention relates to an intelligent target detection system and method for gastroscope images, belonging to the technical field of target detection. The system comprises: an image acquisition module, which used for acquiring a to-be-detected gastroscope image; and a target detection module, which is used for outputting the to-be-detected gastroscope image to a target detection model to obtain a lesion area and a lesion category corresponding to the lesion area. The training process of the target detection model comprises the following steps: determining a plurality of first gastroscope images; determining a label corresponding to each first gastroscope image, wherein the label comprises a lesion category and a lesion area; and inputting the first gastroscope images and the labels corresponding to the first gastroscope images into a convolutional neural network to train the convolutional neural network, thereby acquiring the target detection model. The target detection model is obtained by combining the target detection method with the gastroscope images, so intelligent detection of the lesion area in the gastroscope images and the lesion types corresponding to the lesion areas is realized, and diagnosis errors caused by human subjectivity are reduced.

The embodiments of the invention provide a type B aortic dissection postoperative risk prediction method and device, and an electronic device, and relates to the technical field of medical images. Thetype B aortic dissection postoperative risk prediction method comprises the steps that firstly, a plurality of cases of preoperative three-dimensional angiography images are obtained, and all cases of the three-dimensional angiography images are pretreated to obtain a to-be-treated data set, wherein the to-be-treated data set comprises blood vessel true lumen information and blood vessel false lumen information; then the blood vessel true lumen information and the blood vessel false lumen information are subjected to vector analysis to obtain various image omics feature information, and the various image omics feature information is subjected to dimensionality reduction treatment and linear fitting to obtain risk factor variables; and finally, a risk predicting model is constructed according to the risk factor variables and a preset clinical lesion type. In this way, the risk probability of postoperative complications can be obtained, and thus patient safety is ensured.

The invention discloses a focus fusion method and device, and the method comprises the steps: obtaining a focus set of a breast image; executing at least one focus fusion operation until any two focuses in the focus set cannot be fused into one focus; wherein the step of executing the lesion fusion operation comprises the following steps: determining a first lesion and a second lesion from a lesion set; wherein the first lesion and the second lesion are different types of lesions to be subjected to lesion fusion operation, and the first lesion and the second lesion are lesions which are located in a lesion set according to a preset fusion sequence and are subjected to lesion fusion operation firstly; determining whether the first lesion and the second lesion are fused into a third lesion or not according to the overlapping condition of the area of the first lesion and the area of the second lesion; and if so, adding the third lesion into the lesion set, and deleting the first lesion and the second lesion from the lesion set. According to the technical scheme, the same lesion with various lesion types can be accurately identified, and the working efficiency of doctors is improved.

The invention discloses a method for recognizing a lesion area in a thyroid follicular tumor tissue pathological image, and predicts gene mutation. The method is an automatic auxiliary diagnosis technology based on a deep learning method, a lesion area in a thyroid follicular tumor pathological tissue slice image is automatically positioned by using big data and a deep convolutional neural network algorithm, and a pathological histological type and a gene mutation type of the lesion area are automatically recognized. According to the pathological tissue image, recognizing cases simultaneously carrying RAS and other driver gene mutations, and realizing histological classification of the follicular thyroid tumor and prediction of related gene mutations. According to the method, information is provided for clinicians, pathological diagnosis and clinical decision making are assisted, and development of digital pathology and precise medical treatment is promoted.

The invention discloses a multi-feature fusion image classification method based on deep learning. The method specifically comprises the steps of data set division, data enhancement, classification network model construction, model initialization and model training optimization. And the data enhancement part is used for enhancing data characteristics by randomly carrying out operations such as horizontal overturning, vertical overturning, brightness modification and horizontal overturning according to probability on the picture. In the construction process of the classification network model,the features extracted for the first time are randomly covered and then extracted again, and then the features extracted for the two times are fused, so that the features are diversified, and the classification accuracy is improved. The system can be used for classifying eye malignant tumor images, positioning lesion areas in the images as feature areas, giving out probability values of lesion types and assisting film reading doctors in judgment.

The invention provides an image recognition method, a readable storage medium and electronic equipment, and the method comprises the steps: directly obtaining the lesion type information of a lesion part through a constructed lesion classification model, generating a thermodynamic diagram of a lesion image based on the lesion type information, and determining the specific lesion position information, outputting lesion prompt information according to the lesion type information and the lesion position information; or obtaining the texture and color of the skin of the lesion area through the constructed lesion color classification model and lesion texture classification model, then determining lesion type information through the texture and color of the skin of the lesion area, and generating a thermodynamic diagram of the lesion image based on the lesion type information to determine specific lesion position information; and outputting lesion prompt information according to the lesion type information and the lesion position information. The lesion prompt information can be used as a preliminary judgment of skin disease problems in animal feeding, so that a feeder can know the health condition of the animal at any time.

The invention discloses a premature infant retina plus lesion detection method and system, and the method comprises the steps: firstly inputting a to-be-detected retina posterior pole image into an image quality detection model for quality evaluation, and inputting a qualified image into a blood vessel recognition model to obtain a blood vessel graph; inputting the blood vessel graph into a lesiondetection model, and outputting the probability of the corresponding lesion types of plus, pre-plus and no-plus; and converting the probability of the lesion type of the lesion examination into a corresponding i-ROP lesion score according to a preset score conversion equation to reflect the continuity of the retinopathy degree. According to the invention, the severity of plus lesion is objectively measured, and the severity is tracked over time to provide objective assessment of disease progression or regression; by analyzing the change of the score along with the time, the eyes which progress into plus lesion ROP can be recognized in advance under most conditions for early intervention, disease delay caused by insufficient diagnosis is prevented, the accuracy of lesion degree detection is high, and the ophthalmoscope examination frequency of doctors can be greatly reduced.

The embodiment of the invention discloses an image processing method and device, computer equipment and a storage medium. According to the embodiment of the invention, the medical image of the target object is identified through the lesion identification model, the lesion area of the medical image is determined, then the initial sign information of the lesion area is extracted, the sign information of the lesion area is complemented according to the initial sign information, and the completed sign information is obtained. The lesion type of the lesion area is further determined through the complete sign information, and the accuracy of an image processing result can be improved.