131 results about "Thyroid nodular" patented technology

The invention discloses a benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

The invention relates to the technical field of image classification, in particular to a thyroid nodule ultrasonic image classification method and system based on feature extraction, and the method comprises the following steps: based on thyroid nodule ultrasonic image data, calling a pixel normalization function to adjust a gray scale range to a set interval, screening an isolated noise region, and carrying out the smooth filtering, and acquiring the ultrasonic image after smoothing filtering. According to the method, by adjusting the gray scale range and screening isolated noise areas, gray scale distribution is balanced, noise interference is reduced, image readability is improved, edge change is recognized by analyzing the gradient direction through a small-scale convolution kernel, texture features are extracted through a medium-scale filter, redundancy is reduced in combination with pooling operation, and the feature hierarchical expression ability is enhanced; the edge sharpness parameter is calculated, the feature weight is optimized, the edge sharpness and the classification adaptability are improved, the local feature segmentation precision is improved, the classification boundary is adjusted according to the similarity score, the classification loss measurement is optimized, the error offset is reduced, and the classification stability and accuracy are improved.

The invention discloses a thyroid cancer pathological image classification method and system based on multi-modal feature fusion and Bayesian optimization, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a thyroid ultrasound image data set, and carrying out the preprocessing operation; extracting an improved local binary pattern feature, a Haralick texture feature and a VGG16 depth feature, and constructing a mixed feature space; and splicing the features in the mixed feature space into a 4119-dimensional mixed feature vector, and carrying out feature importance screening by utilizing ExtraTres. Improved local binary pattern features, Haralick texture features and VGG16 depth features are fused, a mixed feature space is constructed, feature importance screening and PCA dimension reduction are performed by using ExtraTrees, multi-level features of the image are effectively extracted, the accuracy of benign and malignant thyroid nodule classification is remarkably improved, meanwhile, network hyper-parameters are dynamically adjusted through a Bayesian optimization algorithm, and the classification accuracy of benign and malignant thyroid nodules is improved. And model convergence is accelerated in combination with a cosine annealing strategy, so that the generalization ability of the model is enhanced, and the model can be excellently expressed on different data sets.

The invention discloses a thyroid nodule segmentation method based on a large model and cooperative consistent training, relates to the technical field of ultrasonic image analysis, and organically embeds the general reasoning ability of the large model into a semi-supervised model to complement the advantages of the semi-supervised model and the large model. The insufficient knowledge migration capability of the semi-supervised model is relieved by utilizing the general prediction capability of the large model, and meanwhile, the high-confidence misprediction of the large model is relieved by utilizing the feature collaborative consistency of the semi-supervised model, so that the actual prediction performance of the model is improved; through large model fine tuning operation under a semi-supervised framework, the large model can be subjected to fine tuning synchronously based on an unsupervised image and a supervised image, and meanwhile, the fine tuning convergence speed and accuracy of the model are accelerated based on collaborative loss and a high-quality pseudo tag; and through collaborative consistency training operation, the advantages of a semi-supervised model and an SAM model are organically complemented, and a high-quality pseudo-label generation scheme and collaborative loss based on self-reliability and collaborative reliability are provided.

The invention relates to the technical field of computer medical detection, in particular to a thyroid nodule benign and malignant classification method based on clinical information, radiomics and gene detection. According to the method, pathological diagnosis of thyroid nodules is used as a dependent variable, detection data including radiomics characteristics and molecular omics are used as continuous variables, and the detection data of at least three genes CLDN10, HMGA2 and LANM3 are selected as the data of the molecular omics; meanwhile, in combination with part of clinical pathological characteristics including gender, age and BRAF V600E mutation condition factors, a thyroid nodule preoperative diagnosis prediction model based on radiomics and molecular omics is constructed through an SVM modeling method. Gene detection, radiomics and clinical basic information are combined, and a thyroid nodule diagnosis model based on the combination is established on the basis of Chinese population. The method is used for assisting clinicians in distinguishing benign and malignant thyroid nodules and guiding clinical decisions

The invention discloses a thyroid nodule ultrasound image segmentation method fusing factorization VMama and characteristic frequency band separation, and relates to the field of computer vision assisted medical treatment. Preprocessing the thyroid nodule ultrasonic image data set; inputting the processed image into an encoder of a segmentation model FMVM-DFFT for processing, extracting image features in a multi-level manner by using an FMVSS module based on VSS, and further finely extracting and fusing feature information under different frequencies by using a feature frequency band separation module DFFT; the method comprises the following steps of: transforming input features into a frequency domain from a space domain by adopting improved fast Fourier transform, decomposing the input features into high and low frequency components, respectively optimizing the high and low frequency components through point-by-point convolution and a channel attention module, recombining the high and low frequency components in a complex domain, and restoring the high and low frequency components to the space domain through inverse Fourier transform; then, through dynamic convolution, layer normalization processing and reserved input features, merging and transmitting to a decoder; the details of the segmentation result are further optimized; the problem that edge segmentation is inaccurate and not fine is solved.

The invention discloses a composition for detecting benign and malignant thyroid nodules and a detection method thereof, and relates to the technical field of medical detection, the composition comprises the following components: a DNA / RNA extraction reagent and a fluorescent quantitative PCR reagent; by combining the DNA / RNA extraction reagent and the fluorescent quantitative PCR reagent, nucleic acid can be efficiently and accurately extracted from the thyroid nodule fine needle puncture sample, specific gene mutation and gene fusion are detected through the fluorescent quantitative PCR technology, the accuracy and sensitivity of benign and malignant judgment of the thyroid nodule are remarkably improved, and the method is suitable for clinical application. And a doctor can formulate a treatment scheme earlier and more accurately, and the prognosis of a patient is improved.

The invention discloses a lightweight multi-feature fusion thyroid nodule classification method. Relates to the technical field of medical image processing, in particular to the technical field of a lightweight multi-feature fusion thyroid nodule classification method. According to the method, the Hifuse model is improved, so that the parameter quantity and the calculation complexity can be reduced while the global features and the local features of the image are fully mined and the benign and malignant thyroid nodules are comprehensively discriminated in a multi-view and multi-azimuth manner. The method comprises the following steps: acquiring and preprocessing a thyroid nodule ultrasonic diagnosis report image data set; a Hifuse model is constructed and improved; a radiomics feature learning module is introduced into the Hifuse model, the local attention module is replaced by the improved local attention module, and the global attention module is replaced by an Officient ViM global extraction module; and completing thyroid nodule classification by adopting the trained Hifuse improved model.

The invention discloses a thyroid nodule segmentation system capable of dynamically fusing multi-echo features, relates to the technical field of artificial intelligence and ultrasonic image analysis, and realizes accurate identification and segmentation of thyroid nodule multi-echo types through cooperative work of three feature branches of low echo, high echo and equal echo. A special feature extraction module is designed for each branch for a specific echo type, the extraction process of different echo nodule features is optimized, the capturing capability of key features is enhanced, and the adaptability of the model to a complex ultrasonic image is remarkably improved; according to the method, adaptive weight distribution of different echo type features is realized through attention weighted fusion of the multi-modal feature maps, the mechanism generates attention weights by using a double-layer full-connection network, and weighted fusion is performed on the feature maps after Softmax normalization, so that the problem of fixed weight or lack of adaptability in a traditional feature fusion method is solved.

The application relates to the field of medical image assisted ablation technology, and provides a thyroid nodule ablation control system based on ultrasound contrast parameters. The system comprises an ultrasound data acquisition module, a contrast parameter analysis module, a blood supply identification module, an ablation strategy generation module, an ablation execution module, a feedback evaluation module, a closed-loop control module and an energy optimization module. By acquiring an ultrasound contrast image of target tissue, perfusion time, perfusion intensity and perfusion sequence parameters are extracted, blood supply arteries or blood supply areas are identified, and a phased ablation strategy is generated, the blood supply area is preferentially ablated, and then main ablation is carried out on the target tissue; at the same time, residual active areas are detected based on contrast feedback, and supplementary ablation is carried out through a closed-loop control mechanism; and ablation parameters are dynamically optimized in combination with a unit volume energy model. The system can improve ablation integrity, reduce the heat sink effect and the risk of intraoperative bleeding, reduce the recurrence rate, and improve energy utilization efficiency.

The invention relates to the field of medical informatics, and provides a biomarker-based thyroid nodule benign and malignant identification method and system. The method comprises the following steps: collecting a serum sample; performing quantitative detection on the multiple markers in the serum sample to obtain concentration data of the multiple markers; inputting the multi-marker concentration data into a pre-constructed probability calculation model to obtain a prediction probability value; and according to a preset diagnosis threshold value and the prediction probability value, judging whether the thyroid nodule is benign or malignant to obtain a thyroid nodule benign or malignant identification result. By integrating multi-marker information, the biological difference between benign and malignant nodules is effectively captured, and the overall efficiency of differential diagnosis is improved.

The application relates to the technical field of data processing, in particular to a thyroid nodule fine-grained automatic identification and classification system based on weak supervision learning, which comprises a server, the server is pre-set with a thyroid fine-grained identification model, the server is used for acquiring an identification image, and a fine-grained identification result output by the thyroid fine-grained identification model according to the identification image; the thyroid fine-grained identification model comprises: a feature pyramid attention module, which is used for processing the identification image according to a feature pyramid attention algorithm; a spatial attention module, which is used for acquiring a binary mask, generating an attention map according to the binary mask, and positioning and discriminating a region; and a multi-network fusion module, which is used for acquiring output results of multiple backbone networks, fusing the output results, and generating a fine-grained identification result. The scheme can solve the technical problem that, when the thyroid nodule is classified in a fine-grained manner in the prior art, the classification accuracy is low due to the multi-scale and multi-network structure of an ultrasonic image.

The invention provides a methylation marker combination for identifying and diagnosing benign and malignant thyroid nodules or diagnosing thyroid cancer. The methylation marker combination comprises methylation TSHR, NIS and TPO genes or fragments thereof. In addition, the invention further provides a thyroid cancer diagnosis product and device based on the methylation marker combination and application of the thyroid cancer diagnosis product and device in thyroid nodule benign and malignant identification and thyroid cancer diagnosis. Whether a thyroid tissue sample or a blood sample is taken as a detection object, the methylation marker combination can realize efficient and sensitive diagnosis of thyroid cancer, and especially has outstanding performance in early thyroid cancer diagnosis, so that the methylation marker combination has a good application prospect. And the kit has important clinical application value and practical significance for early screening and diagnosis of thyroid cancer.

The invention provides a thyroid disease multi-omics mass spectrum construction analysis method and system.The thyroid disease multi-omics mass spectrum construction analysis method comprises the steps that a thyroid ultrasound image and multi-omics mass spectrum data are obtained, and a cross-modal incidence relation is generated based on anatomical structure characteristics of the thyroid ultrasound image and biomolecule space distribution characteristics of the multi-omics mass spectrum data; in combination with the thyroid anatomical structure deformation field and a multi-task optimization target, performing nonlinear coupling calculation on a back propagation error in a multi-task learning iteration process to obtain a gradient contribution weight between tasks; and generating a target multi-omics mass spectrum based on the cross-modal correlation features, the anatomical structure deformation field and the gradient contribution weight, and performing thyroid pathological feature analysis on the target multi-omics mass spectrum to obtain a three-dimensional positioning coordinate and a malignancy probability prediction value of the thyroid nodule. The thyroid disease diagnosis comprehensiveness and accuracy are improved.

This invention relates to the field of traditional Chinese medicine and natural medicines, and discloses a traditional Chinese medicine composition for treating thyroiditis, nodular goiter, and thyroid nodules, as well as its preparation method and application. The traditional Chinese medicine composition is made from raw materials in parts by weight of 1-30 parts of Prunella vulgaris, 1-30 parts of Taraxacum mongolicum, 1-30 parts of Hordeum vulgare malt, 1-20 parts of Citrus reticulata peel, 1-24 parts of Crataegus pinnatifida, and 1-24 parts of Raphanus sativus seed. It can not only significantly reduce serum TgAb and TPOAb antibody levels, inhibiting the attack of autoimmune response on the thyroid gland from the source, but also effectively reduce lymphocyte infiltration, repair damaged thyroid follicular structure and restore colloid content, thereby reversing immune damage and tissue destruction of the thyroid gland, reducing thyroid nodules, and solving the problems of existing treatments for thyroiditis, nodular goiter, and thyroid nodules that cannot reverse immune damage to the thyroid gland, destruction of follicular structure, and tissue fibrosis.

The invention discloses a combined medicine composition of a thyroid Qixia patch and a selenium yeast tablet and application of the combined medicine composition, and belongs to the technical field of medicines. The composition is composed of a thyroid and astragalus mongholicus summer patch and a selenium yeast tablet, the thyroid and astragalus mongholicus summer patch is prepared by grinding astragalus mongholicus, selfheal, radix clematidis, turmeric, airpotato yam and semen brassicae as raw materials, and adding vaseline to prepare paste. The selenium yeast tablet is used for oral administration and is matched with the thyroid qi-summer patch for external application on the neck, and the treatment course is 3 months. Clinical verifications show that the total effective rate of the composition reaches 87.80%, the composition can effectively improve symptoms such as anterior cervical mass, dysphoria, irritability and the like, reduce the level of serum TPOAb, reduce thyroid and nodule volumes and improve nodule blood flow, the safety is good, the curative effect is superior to that of pure western medicine treatment, and an effective treatment scheme is provided for related diseases.

The application belongs to the technical field of molecular biology and medicine, and discloses a methylation marker for detecting the benignity and malignancy of thyroid nodules and an application. 780 new methylation sites are identified, 273 tumor occurrence and development related genes are associated, and the Fc gamma R-mediated phagocytosis signal pathway is involved. The methylation marker for detecting the benignity and malignancy of thyroid nodules comprises multiple genes and multiple methylation sites. The methylation level of multiple genes and multiple sites is detected, the problem of low and unstable methylation signal of single gene or single site is overcome, and thus the sensitivity and specificity of detection are improved.

The invention discloses a marker combination for thyroid nodule risk assessment and application thereof. Based on large-sample multi-omics sequencing data, papillary thyroid carcinoma (PTC) and benign nodules are found to have a common epithelial spectrum origin, epithelial intermediate state cells (EICs) with malignant potential are found in the benign nodules, and typical molecular markers of the EICs are further identified to comprise ZCCHC12, CDLN1 and NPC2. The three genes participate in proliferation and differentiation of cells, the expression in PTC is remarkably up-regulated, and the malignant risk of the benign thyroid nodules can be evaluated by detecting the expression level of the markers in a biological sample. According to the method, a means for recognizing the high-risk benign nodules before malignant transformation occurs is provided for the first time, risk perspectiveness is achieved, and higher specificity and sensitivity are possibly achieved for recognizing the benign nodules with malignant potential.

The application relates to the technical field of ultrasonic image analysis, and discloses a thyroid nodule evaluation system based on ultrasonic images. The system obtains medical record data and medical examination data of a patient through a multidimensional acquisition module, classifies and forms a data set, analyzes the thyroid function state of each patient through an intelligent evaluation module, generates a function coefficient, analyzes the picture quality of each group of ultrasonic images again, generates corresponding detection data group Jcsj, establishes a unified evaluation standard, quickly screens out key frames from a large amount of ultrasonic image data, has high multidimensional integration efficiency, sets a fixed range threshold value for the intelligent evaluation module, analyzes and generates a nodule volume and a nodule score in combination with a medical data set, and in combination with the function coefficient, the detection data group and the nodule score, evaluates the risk grade of the thyroid nodule and the picture quality of the ultrasonic image of the patient, screens out key frames of the ultrasonic image, and outputs a treatment suggestion; and the intelligent evaluation has high precision.

This invention provides a method for analyzing the radiomics characteristics of medullary thyroid carcinoma based on ultrasound, relating to the field of image or video recognition or understanding technology. The method includes: obtaining data from thyroid surgeries at various target hospitals to form a backup dataset; filtering the backup dataset to obtain a target dataset; labeling the data in the target dataset with Regions of Interest (ROIs); performing feature extraction and feature filtering on the data in the target dataset to obtain training and validation sets; constructing a target radiomics feature model; calculating radiomics feature scores based on the target radiomics feature model; obtaining a clinical feature model, an ultrasound feature model, a comprehensive model, and a scoring model based on logistic regression analysis; and performing radiomics feature analysis based on the clinical feature model, ultrasound feature model, comprehensive model, and scoring model. This invention solves the problems of low diagnostic accuracy and high requirements for experience of image interpreters in existing technologies for thyroid nodules.

The invention relates to the technical field of thermal ablation, in particular to a thyroid nodule ultramicro-invasive ablation precise puncture positioning method, which comprises the following steps of: acquiring continuous gray frames and extracting tissue gray to complete normalization and phase demodulation to generate phase time data; detecting a brightness peak value according to phase time data to obtain a tip echo, calculating a phase gradient in a neighborhood by using a fixed window to generate a stable domain data set, calculating a spatial range according to a stable domain, extracting a phase change signal along with time, and completing frequency domain dominant frequency extraction and period analysis to generate a period consistent region; according to the method, the continuous image phases are extracted, the time sequence is combined, the needle point recognition stability is enhanced, the echo region is restrained through the brightness peak value and the phase gradient, and the accuracy of positioning is improved. Position judgment is enhanced through spatial range and period analysis, and the guiding precision is improved by combining a three-dimensional distance and a symbol sequence.

This invention relates to the technical field of ultrasound image feature extraction for thyroid nodules, specifically to a method and system for ultrasound image feature extraction of thyroid nodules. The method includes the following steps: acquiring an ultrasound image containing thyroid nodules; performing multi-scale image analysis on the ultrasound image to construct a multi-scale image representation of the ultrasound image; using the multi-scale image representation to identify low-echo regions in the ultrasound image, and combining feature analysis, judgment correction, and spatial relationship verification to determine the acoustic shadowing region in the ultrasound image; performing suppression processing on the acoustic shadowing region, wherein the suppression processing includes unifying the signal within the acoustic shadowing region and softening the boundary of the acoustic shadowing region to obtain an optimized image; identifying the contour of the thyroid nodule based on the optimized image; and quantifying the morphology and internal structure of the thyroid nodule based on its contour. This application can effectively identify and suppress acoustic shadowing regions in ultrasound images.

The invention provides a thyroid nodule auxiliary diagnosis model construction method and system based on multi-modal data and a storage medium. The method comprises the following steps: acquiring multi-modal thyroid nodule medical data; performing classification processing on the thyroid nodule medical data of each mode by using a single-mode classification model to obtain each thyroid nodule intermediate classification result corresponding to the thyroid nodule medical data of each mode; and carrying out weighted fusion on all the thyroid nodule intermediate classification results to obtain thyroid nodule benign and malignant classification results based on the multi-modal thyroid nodule medical data. According to the embodiment of the invention, the single-mode classification model is adopted to perform classification processing on the medical data of each mode, so that the step of converting the multi-mode data into the structured data is omitted, and the data processing efficiency is higher; in addition, the thyroid nodules can be diagnosed, monitored and treated, clinicians are assisted in improving the diagnosis efficiency of the thyroid nodules, and clinical burdens are reduced.

1. The name of the design product: neck protection (graphene). 2. The use of the design product: the design product is used for the treatment of cervical spondylosis and thyroid nodules. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention provides a traditional Chinese medicine composition for treating thyroid nodules, a pharmaceutical preparation and a preparation method thereof, and relates to the technical field of medicines. The traditional Chinese medicine composition comprises the following components: thunberg fritillary bulb, pinellia ternate, curcuma zedoary, polygonum cuspidatum, wild buckwheat rhizome, scutellaria baicalensis, cortex albiziae, honey loquat leaf, bighead atractylodes rhizome, epimedium, Chinese yam and liquorice. The traditional Chinese medicine composition and the pharmaceutical preparation provided by the invention have the effects of reducing phlegm and resolving masses, promoting blood circulation and removing blood stasis, warming and tonifying spleen and kidney, and soothing liver and soothing nerves, and have a good treatment effect on thyroid nodules.

This invention relates to the field of image analysis technology, specifically to an automatic classification method for benign and malignant thyroid nodules based on multimodal ultrasound imaging. The method involves acquiring images of thyroid nodules; given that the invasive growth of cancer cells in malignant nodules can damage cell structure and induce grayscale mutations, the nodule region is segmented and divided into sub-regions. The grayscale similarity of sub-regions is analyzed to quantify local heterogeneity, yielding spatial grayscale performance indicators; then, the correlation between these indicators and the proportion of blood flow pixels is analyzed to establish a structural-blood flow abnormality association model, obtaining the blood flow supply matching degree; further, based on shape and edge grayscale fluctuation analysis, a structural disorder index is obtained, infiltration candidate areas are located, and blood flow is analyzed to obtain peripheral invasive blood flow indicators. Combining the correlation between these two indicators with the blood flow supply matching degree, a structure-blood flow coupling coefficient is obtained; finally, multiple indicators are integrated to obtain a malignancy risk score, and a classification neural network is trained to classify the risk level of samples, assisting doctors in making refined judgments.

The application discloses a kind of based on mixed domain enhancement's thyroid nodule semi-supervised segmentation method and device, it is related to image segmentation technical field.The method includes: constructing semi-supervised segmentation model, including unified bidirectional copy and paste enhancement module, cross-teaching model, patch enhancement module based on information entropy and frequency domain enhancement module based on Fourier transform;Using labeled data and unlabeled data to train the model constructed, by unified bidirectional copy and paste, generate intermediate sample between labeled data and unlabeled data in space domain;Using the uncertainty estimation based on information entropy, exchange low-entropy patch in labeled data and high-entropy patch in unlabeled data;The low-frequency component of labeled data and unlabeled data is exchanged by fast Fourier transform, and the low-frequency prediction is supervised in consistency based on the pseudo-label generated based on full frequency;The thyroid nodule ultrasound image to be segmented is input into the trained segmentation model, and the nodule segmentation result is obtained.The present application can improve segmentation accuracy.

The application discloses a thyroid nodule image classification method based on ultrasound and infrared thermal image multimodal images. The application is based on ultrasound and infrared thermal images, combined with a multimodal learning method, and provides a self-adaptive multimodal hybrid model, which is composed of an intra-modal mixed encoder, a self-adaptive cross-modal encoder and a classification head. The intra-modal mixed encoder can model global features while extracting local features; the self-adaptive cross-modal encoder can formulate personalized modal weights according to different cases, and simultaneously perform information interaction and fusion of inter-modal features; and the classification head classifies the obtained fusion features. The method automatically classifies the thyroid nodule images of a subject based on ultrasound and infrared thermal images of the subject by using an AmmH model, and provides objective and accurate classification results for doctors, so that auxiliary diagnosis is realized.

The invention discloses application of daphnetin in thyroid nodule treatment medicines, relates to the field of medicines, in particular to the technical field of new application of medicines, can be used for thyroid nodule treatment, and has a wide application prospect.