317 results about "Abnormal thyroid" 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 medicine, and discloses a thyroid nodule surgical operation planning and navigation system based on artificial intelligence, which comprises a multi-modal medical image acquisition module, an image preprocessing and enhancing module, an intelligent anatomical structure analysis module, an operation planning decision module and an intraoperative real-time navigation module, the multi-modal medical image acquisition module is used for integrating data of various image equipment such as ultrasonic equipment, CT equipment and MRI equipment, and space-time consistency of multi-modal images is ensured. According to the thyroid nodule surgical operation planning and navigation system based on artificial intelligence, the real-time calculation problem is solved, the system delay is reduced, which is crucial to real-time navigation in an operation, the real-time performance and the operation efficiency are improved, the operation time is shortened, and complications are reduced; by integrating the advantages of different images, the system can provide more comprehensive dissection views, help doctors to more accurately position nodules and important structures and avoid injury to nerves and blood vessels, so that the accuracy is improved, and the surgical risk is further reduced.

The invention relates to the technical field of computer vision, and discloses a thyroid ultrasonic robot automatic scanning method, device and equipment based on RGB images and depth information and a medium, and the method comprises the steps: obtaining image information and depth information, coding the depth information, fusing and recognizing a target scanning area, determining an initial scanning point and an initial scanning direction, controlling the scanning probe to scan and collect a real-time scanning image, analyzing the real-time scanning image to recognize a preset target and an artifact area, adjusting a scanning posture and a scanning path based on a recognition result, monitoring a continuous existence state of the preset target, and stopping scanning when the preset target is not recognized continuously. The target area is identified by fusing the multi-modal image information, the scanning posture and path are dynamically adjusted in combination with real-time image analysis, scanning termination is intelligently controlled according to the target detection result, the positioning accuracy, image quality and standardization level of ultrasonic scanning are improved, and the method is suitable for automatic ultrasonic imaging of thyroid and superficial organs.

The invention discloses a thyroid intraoperative real-time navigation method and a thyroid intraoperative real-time navigation system based on multi-mode optical fusion. The thyroid intraoperative real-time navigation method comprises the following steps: outputting visible light through an endoscope and coupling light waves of a narrow-band multispectral light source to irradiate an operative field, exciting parathyroid glands to generate near-infrared fluorescence, and receiving reflected visible light, split light, near-infrared fluorescence and laser speckle signals through an endoscope probe. And separating the composite optical signal into four channels, and respectively generating an anatomical structure color image, a blood vessel spectroscopic image, a parathyroid gland near-infrared fluorescence image and a laser speckle image. Performing decorrelation processing on the laser speckle image to generate a blood flow dynamic pseudo-color decorrelation speckle image; an anatomical structure, a blood vessel center line, a parathyroid gland contour and blood flow dynamic feature points are extracted through a multi-modal registration technology, after affine transformation space alignment is conducted, a comprehensive imaging map containing the anatomical structure, blood vessel distribution and parathyroid gland function marking information is generated through a wavelet fusion algorithm, and intraoperative multi-dimensional real-time tissue navigation is achieved.

The invention discloses a thyroid tumor diagnosis method and system based on ultrasonic and cytological image conjoint analysis. The method comprises the following steps: converting a thyroid B ultrasonic image of a patient to be diagnosed into an image feature vector FUS; performing structured feature extraction on the thyroid cytological image of the patient to be diagnosed to obtain a structured feature vector FCYTO; the image feature vector FUS and the structured feature vector FCYTO are converted into a fusion Token sequence; and inputting the Token into a multi-modal feature fusion and prediction network based on a Transform architecture, and carrying out feature fusion and classification prediction so as to obtain the probability that the thyroid tumor of the patient to be diagnosed is malignant. The thyroid tumor diagnosis based on multi-modal fusion is carried out on the basis of ultrasonic and cytological images, so that the diagnosis accuracy is 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 an automatic diagnosis method of a thyroid pathological image, and belongs to the technical field of medical pathological image auxiliary intelligent diagnosis, and the method comprises the following steps: constructing a thyroid tumor pathological image data set, preprocessing a pathological image, carrying out multi-scale fusion, carrying out feature extraction, carrying out feature fusion, inputting a CLAM-SB multi-instance learning model, and generating a visual heat map. The invention provides a thyroid pathology image automatic diagnosis method based on weak supervision multi-instance learning and multi-scale feature fusion, which can effectively improve the accuracy of pathology tissue region segmentation and pathology image classification, reduce the workload of pathologists and improve the diagnosis efficiency and reliability.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The invention relates to the technical field of health monitoring, in particular to a thyroid dysfunction detection system based on big data, which comprises a hormone data management module, a trend inflection point identification module, a section structure judgment module, a label dynamic updating module and a periodic evolution evaluation module. According to the method, continuous modeling is carried out on hormone concentration dynamic characteristics through multi-time-point data time sequence arrangement in hormone detection records, local extreme points of hormone values are recognized, key fluctuation inflection point positions are defined, an abnormal fluctuation range is recognized, and a reference interval ratio judgment mechanism is introduced to improve the abnormal recognition precision; the dynamic tracking of the function state change process is realized by recording the label evolution trajectory through the continuous state label comparison and updating process, the identification capability of potential function abnormality is enhanced by utilizing the quantitative evaluation of the individual state fluctuation stability, the identification efficiency and the monitoring timeliness of the key intervention individual are improved, and the monitoring accuracy is improved. And the continuity of thyroid function state change identification and the accuracy of intervention target identification are improved.

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 thyroid image segmentation method and system for obtaining multi-scale feature fusion based on UNet and deformable convolution, and belongs to the technical field of medical image processing and computer vision. A model DAmUNet of a deformable convolution and multi-scale attention feature fusion module is introduced into a UNet network architecture, the deformable convolution is adopted in an encoder and a decoder to replace traditional convolution, in the deepest layer of the encoder, the multi-scale attention feature fusion module is adopted, encoding features of the encoder with different resolutions are used as input, and the multi-scale attention feature fusion module is used as input. Channel weighting is carried out on the deepest layer coding features through a double-input channel attention mechanism to generate fusion features; and taking the fusion features as decoder input, and finally obtaining a pixel-level segmentation result of the thyroid region. The method effectively improves the segmentation precision and robustness, is excellent in Dice, IoU and other indexes, and can be widely applied to the scenes of thyroid volume calculation, preoperative evaluation and the like.

The invention provides a thyroid ultrasonic signal intelligent analysis and diagnosis method and system, and relates to the technical field of ultrasonic signal analysis, and the method comprises the steps: calculating different depth noise distribution, automatically adjusting a filtering parameter value, and carrying out the filtering through anisotropic diffusion; segmenting the processed image to obtain a nodule region; acoustic characteristic data, acoustic characteristic parameter data and image characteristic data are extracted to form a comprehensive characteristic vector, and dimension reduction is carried out; and calculating a feature association weight for optimization to obtain a classification result, and generating a diagnosis report. The thyroid ultrasound image analysis precision is improved, and intelligent diagnosis of benign and malignant nodules is achieved.

The invention discloses a thyroid nodule benign and malignant prediction method based on automatic segmentation and semi-supervised learning. The thyroid nodule benign and malignant prediction method comprises the following steps of data set establishment, image preprocessing, thyroid nodule segmentation model training, mask and image fusion processing, thyroid nodule benign and malignant classification model training and semi-supervised learning model construction. According to the method, the thyroid nodule effective area is segmented firstly, subsequent benign and malignant classification tasks are assisted, and information space is expanded and learned through semi-supervised learning, so that the problems of high medical image marking cost, insufficient data and limitation on deep learning model performance in the prior art are solved; and meanwhile, the second-generation basic model of the sliding window layered visual converter after mixing fusion is carried out on the basis of pre-training and by using the nodule region generated by the thyroid segmentation model realizes good performance, and high-precision thyroid nodule benign and malignant classification prediction can be realized.

The invention relates to a primer composition, kit and method for detecting benign and malignant thyroid nodule related gene mutation, and the primer composition comprises a primer pair for detecting benign and malignant thyroid nodule related gene DNA mutation and RNA fusion variation. The primer pair can be independently amplified, and can be used for performing efficient, high-sensitivity and high-specificity multiple amplification on DNA and cDNA templates to be detected under the same PCR reaction system and reaction program, and then the gene mutation condition of a related region can be obtained by constructing a library and sequencing, so as to assist clinical diagnosis of benign and malignant thyroid nodules. Moreover, specific nucleotide sequences are respectively connected to the 5'end of the upstream primer and the 3 'end of the corresponding downstream primer in a proper proportion in the primer composition, so that the homogeneity and specificity of multiple PCR amplification can be further improved, and the detected nucleic acid input amount and the lowest detection limit of mutation frequency (as low as 0.5%) are effectively reduced.

The invention relates to the technical field of machine learning, and discloses a thyroid disease risk prediction method and system based on machine learning. The method comprises the steps of collecting medical record data of a thyroid disease patient, establishing a classification model based on the medical record data, and outputting a thyroid disease type corresponding to the medical record data; establishing a word vector model and a machine learning model, extracting a first feature quantity and a second feature quantity from the medical record data, screening out a first key feature and a second key feature of each thyroid disease from the first feature quantity and the second feature quantity, fusing the first key feature and the second key feature to obtain a comprehensive feature vector, and establishing a risk prediction model; acquiring medical record data of the current observation object, inputting the medical record data into the risk prediction model, and predicting and generating a thyroid disease risk level and a disease type of the current observation object based on the medical record data; if the risk level is a medium risk or a high risk, an optimal adjustment vector is generated, health adjustment suggestions are obtained based on the optimal adjustment vector, and the prediction accuracy of the thyroid disease risk is improved.

The invention discloses a disease diagnosis method and system based on multi-modal space-frequency domain adaptive fusion, and relates to the field of artificial intelligence and biomedical engineering.The method comprises the steps that multi-modal data are standardized, the unified and standardized multi-modal data are coded, and multi-modal initial feature representation is obtained; after projection and gating alignment and cross-modal interactive attention alignment are carried out on the initial feature representation of each modal, enhanced representations of each modal are obtained, and then the enhanced representations of each modal are fused into a shared feature representation; performing deep feature extraction on the enhanced representation of each mode to obtain deep features of each mode, and performing adaptive multi-domain feature enhancement processing to obtain multi-domain enhanced features of each mode; performing semantic alignment on the multi-domain enhanced features of each mode, and then performing fusion through a hierarchical attention mechanism to obtain fusion features; and the fusion features are input into a diagnosis network for prediction, a disease diagnosis result is obtained, and the intelligent diagnosis precision and robustness of papillary thyroid carcinoma are improved.

The invention discloses a thyroid ultrasound image nodule segmentation method based on spatio-temporal characteristics and frequency enhancement, and relates to the technical field of medical image processing, and the method comprises the following specific steps: multi-scale characteristic extraction: carrying out the gray normalization and median filtering preprocessing of a thyroid ultrasound image, and carrying out the segmentation of the thyroid ultrasound image nodule; extracting multi-scale features under different resolutions by using a pre-trained backbone network, obtaining the multi-scale features under different resolutions by the backbone network through multilayer convolution and pooling operation, and generating basic and advanced features through convolution operation; by designing an intra-frame feature extraction architecture based on frequency feature fusion and an efficient spatial-temporal feature aggregation mechanism, intra-frame and inter-frame collaborative optimization is realized, intra-frame and inter-frame high and low frequency features are solved by means of octave convolution, the perceptual ability of a model to ultrasonic image details and semantic information is improved, inter-frame and inter-frame feature fusion is realized, and the perceptual ability of the model to ultrasonic image details and semantic information is improved. A local circulation neighborhood propagation mechanism is adopted, and interference of thyroid movement on time sequence feature extraction is effectively restrained.

The invention relates to the technical field of image processing, in particular to an ultrasonic thyroid nodule feature pyramid edge weighted diffusion segmentation method, which comprises the following steps: acquiring an ultrasonic thyroid nodule image; a PEW-SegDiff model is constructed; gaussian noise is added through time in the forward diffusion process of the real mask image, and a label with noise is obtained; in the back diffusion process, the input image and the real mask image after forward diffusion are added to obtain a noise label feature image; training a PEW-SegDiff model by using the noise tag feature graph and the input graph to obtain a predicted initial image; and calculating the loss between the predicted image and the back diffusion image of the real mask. The thyroid nodule edge blurred image extraction method solves the problems that an original SegDiff model is limited in boundary learning capability and the effect is poor when thyroid nodule edge blurred images are extracted.

The invention discloses a thyroid ultrasound diagnosis report intelligent generation method based on a multi-mode large language model, and relates to a thyroid ultrasound diagnosis report intelligent generation method. The objective of the invention is to solve the problems of lack of term standardization and insufficient complex focus feature analysis in the prior art. According to the method, a full-flow technical system of double-flow coding, cross-modal alignment, dynamic man-machine cooperation and multi-dimensional evaluation is constructed. Multi-scale feature fusion of a thyroid global form and a nodule ROI region is realized through ResNet-50 and ConvNeXt double-flow coding networks, image-text semantic alignment is optimized by adopting a CLIP symmetry loss function, and training resource consumption is reduced in combination with an LoRA parameter fine tuning technology. A dynamic man-machine collaborative closed-loop mechanism is innovatively introduced, model parameters are iteratively optimized through doctor correction data, and a four-dimensional clinical evaluation system comprising ROUGE-L, BLEU-4, CIDEr and expert blind evaluation is established. The invention belongs to the technical field of medical artificial intelligence auxiliary diagnosis.

The invention provides a special endoscope thyroid nerve monitoring bipolar electrode, and relates to the technical field of medical apparatuses and instruments. A radio frequency output electrode is arranged in a functional electrode unit and used for applying high-frequency current to carry out tissue cutting and blood coagulation, and a nerve stimulation electrode is used for applying low-frequency current to carry out nerve stimulation and monitoring. The two electrodes work cooperatively, the impedance detection electrode can collect a voltage signal after the low-frequency current passes through the tissue, and the impedance modeling unit converts the collected voltage signal to obtain an impedance value. The position positioning module determines the position of a working point between the two electrode ends. In the operation process, the initial impedance value of the working point at the current position serves as the type impedance to judge the tissue type of the working point, tissue cutting is conducted along with a radio frequency output electrode, the impedance value change of the working point at the current position is monitored, and the tissue cutting result is judged in combination with the previously determined tissue type. And the safety and the overall efficiency of the surgical operation are improved.

The invention discloses a thyroid nodule image classification method. The thyroid nodule image classification method comprises the steps of obtaining an image of an existing thyroid part and performing preprocessing to construct a training data set; constructing a thyroid nodule image classification initial model based on the convolution module, the attention mechanism and the full connection layer, and training to obtain a thyroid nodule image classification model; and classifying actual thyroid nodule images by using the obtained thyroid nodule image classification model. The invention also discloses an imaging method comprising the thyroid nodule image classification method. The thyroid nodule image classification model is constructed and trained based on the convolution module, the attention mechanism and the full connection layer, classification and imaging of the thyroid nodule images are achieved, and the thyroid nodule image classification method is higher in reliability, better in accuracy and better in applicability.

The invention relates to the field of medical health management, and discloses a thyroid-oriented whole-cycle health management method, which comprises the following steps of: firstly obtaining thyroid multi-source information data of a target population to form a data set, then extracting fusion features to generate a time sequence feature vector, and analyzing and calculating occurrence risk factors and scores of feature dimensions in a preset time window according to the time sequence feature vector; a comprehensive risk score is obtained through time sequence analysis, risk grades are divided, high-risk individuals are marked, and a personalized screening strategy is generated; then combining historical data of high-risk individuals, analyzing key driving paths with high risk scores, associating an external knowledge base to predict recurrence frequency, and finally dynamically adjusting and optimizing screening and intervention strategies according to the recurrence frequency; precise evaluation, grading and personalized management of individual thyroid health risks are achieved, then risk key driving paths are found out, recurrence is predicted, and the strategy is dynamically optimized to improve the disease prevention and treatment effect.

The present disclosure provides lysosomal targeting bifunctional molecules that target disease causing anti-TSHR autoantibodies for degradation. The lysosomal targeting bifunctional molecules include a ligand moiety that specifically binds to an asialoglycoprotein receptor (ASGPR), and which is linked via a carrier protein to a thyroid stimulating hormone receptor (TSHR) polypeptide that specifically binds target anti-TSHR autoantibodies.

The invention provides a thyroid-related eye disease intraorbital soft tissue volume measurement method and system, and is applied to the technical field of data processing. Three-dimensional medical image data and system abnormal state information are obtained, the three-dimensional medical image data comprise CT images and MRI images, and the system abnormal state information comprises artifacts, isolated noisy points and irregular boundaries; data preprocessing is carried out on the three-dimensional medical image data, standardized image data is generated, and the standardized image data is formed by carrying out ternary processing on spatial registration, noise suppression and gray level normalization; processing the standardized image data and the system abnormal state information to generate an initial segmentation mask; processing the initial segmentation mask to generate an optimized mask; processing the optimized mask and the three-dimensional medical image data to generate soft tissue volume data; and processing the soft tissue volume data, the system abnormal state information and the historical volume data to generate a volume dynamic change report.

The invention discloses a group of blocking monoclonal antibodies of an SH1 homologous cluster fully human thyrotropin receptor (TSHR) (a TSH receptor, TSHR) as well as a preparation method and application of the blocking monoclonal antibodies of the SH1 homologous cluster fully human thyrotropin receptor (TSHR). The method comprises the following steps: sorting plasma cells and memory single B cells for specifically recognizing TSHR in peripheral blood of a patient with high TSH receptor blocking antibody (TBAb) titer by using flow cytometry, cloning antibody light and heavy chains in vitro and performing recombinant expression, and performing antibody property screening verification by using hTSHR-CHO cells. The blocking monoclonal antibody specifically targeting the human TSHR is obtained. The fully human TSH receptor blocking monoclonal antibody disclosed by the invention can be specifically combined with TSHR, and can effectively block signal transduction after the TSH is combined with the receptor; the synthesis and secretion of thyroid hormone are inhibited; expression and fibrosis of effector cell orbital fibroblasts TSHR for inhibiting thyroid-related eye diseases are remarkably relieved, and wide application prospects are achieved in treatment of Graves' diseases and diseases such as thyroid eye diseases caused by hyperthyroidism.

The thyroid nodule classification system based on the ultrasonic radio frequency signal comprises a data acquisition module, a model construction module and a classification module. The data acquisition module is used for acquiring ultrasonic radio-frequency signal data of thyroid nodules, preprocessing ultrasonic radio-frequency signals to form an instance set, and constructing a data set with labels. The model construction module is used for constructing a neural network model for performing feature classification on the thyroid nodules and training the neural network model based on the data set; the neural network model comprises a feature conversion layer, a dynamic weight generation layer, a self-attention layer and a classification probability fusion layer. And the classification module is used for carrying out feature classification on the thyroid nodules based on the trained neural network model. The method has the advantages that the original ultrasonic radio-frequency signals are directly analyzed, so that information loss caused by image reconstruction is effectively avoided, key information in the ultrasonic radio-frequency signals can be accurately captured and analyzed, and high-precision classification of thyroid nodules is realized.

The invention belongs to the cross field of multiple subjects such as artificial intelligence, computer vision, natural language processing and medical image processing, and discloses a thyroid ultrasound-assisted diagnosis method and system based on text-driven vision pre-training and cross-modal feature fusion. Wherein the model building and training stage comprises the following steps of: preprocessing to obtain an unlabeled image-text pair data set and a labeled image-text pair data set; an image encoder and a text encoder are constructed, pre-training is carried out, and text-driven visual pre-training is realized; using a cross-modal splicing attention fusion module (CMCAF) to realize fusion of text features and image features, inputting the fused text features and image features to a full-connection layer classifier, and performing training optimization through supervision; the application stage is to predict benign and malignant thyroid nodules for the to-be-predicted image-text pairs. The medical image multi-modal fusion method has the advantages of high accuracy, low dependence on pathological labels, high model interpretability, good clinical deployment feasibility and the like, and the problem of the existing AI model in the aspect of medical image multi-modal fusion is effectively solved.

The invention discloses a thyroid-related eye disease auxiliary verification method based on SPECT / CT and federal learning. The thyroid-related eye disease auxiliary verification method comprises the steps that A, image data standardization and orbit area extraction are carried out; b, extracting a preliminary segmentation mask of each rectus in the CT image; c, fusing the structure prior mask, and obtaining an extraocular muscle fine segmentation mask through multi-mode collaborative segmentation; d, utilizing extraocular muscle fine segmentation masks, SPECT and CT images, fusing segmentation prior and multi-modal features to perform deep discrimination, performing automatic prediction of thyroid-related eye disease activity staging, and outputting a prediction probability; and E, based on a dynamic model combination strategy of style similarity and uncertainty quantization, obtaining a dynamic combination model of segmentation and classification adapted to new domain feature distribution, and predicting TAO activity. According to the method, accurate segmentation of extraocular muscle structures and accurate prediction of TAO activity in an invisible data center can be realized, auxiliary verification data support is provided for diagnosis of thyroid-related eye diseases, and the diagnosis accuracy is improved.

The invention discloses a thyroid tumor evaluation system based on combination of an ultrasonic image and pathological data, and relates to the technical field of auxiliary decision making. Comprising the steps that an image acquisition module preprocesses a thyroid ultrasound image; the pathology acquisition module is used for extracting structured data containing pathology malignancy scores and molecular marker expression parameters; the feature extraction module generates feature vectors from the images and the pathological data respectively; the region screening module dynamically generates an image region sensitivity threshold value based on the molecular marker and the pathological malignant score; a weighted extraction module recognizes an image suspicious region and generates weighted features according to the image suspicious region; the multi-modal fusion module fuses bimodal features through a weight distribution rule; and the evaluation decision module outputs benign and malignant prediction results and confidence scores. Dynamic sensitivity regulation and control of deep coupling of pathological semantics and image features are realized, and image suspicious region identification precision and cross-modal decision reliability precision are 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.