536 results about "Skin lesion" patented technology

The invention discloses an efficient skin disease segmentation method based on multiple scales and a mixed attention mechanism, and the method comprises the steps: firstly carrying out the preprocessing of a skin mirror image through data enhancement, and then inputting the skin mirror image into a convolutional neural network for feature extraction, thereby obtaining a skin lesion segmentation region prediction map. According to the method, the convolutional neural network is applied, multi-scale features of local information and global information can be extracted from a skin mirror image, and the feature representation capability is enhanced through feature fusion. The objective of the invention is to solve the problems of changeable lesion areas, irregular shapes and the like of skin disease images, and improve the accuracy and robustness of skin disease image segmentation.

The invention discloses a multi-level feature fusion medical image segmentation method and system based on a diffusion model, and belongs to the technical field of medical image processing. The method comprises the following steps: acquiring an original medical skin lesion image and preprocessing the image, wherein the preprocessing comprises size adjustment and normalization; gaussian noise is added to the input image through the forward diffusion process, and the image with the noise added is obtained; constructing a denoising network encoder, and inputting the preprocessed original input image and the noise image into the denoising network encoder; and constructing a de-noising network decoder, sending the output features of the encoder into the decoder through jump connection and a multi-level feature fusion module, and outputting a segmentation result graph. According to the invention, through a boundary perception loss function and a multi-level feature fusion mechanism, the segmentation precision is improved, and the robustness of the model to noise is enhanced.

The invention discloses an infrared lamp therapeutic instrument control system capable of automatically controlling therapeutic parameters, and relates to the technical field of infrared lamp therapeutic instruments. The infrared lamp therapeutic instrument control system capable of automatically controlling the therapeutic parameters comprises a data acquisition and processing unit, a skin lesion identification and severity evaluation unit and a treatment and feedback management unit, and through real-time data acquisition and processing, the system can accurately identify the type and severity of skin lesion, thereby providing a scientific basis for subsequent treatment. Meanwhile, the treatment and feedback management unit continuously updates treatment parameters according to real-time monitoring data, the working state of the infrared lamp is automatically adjusted, and the safety and effectiveness of the treatment process are ensured. Besides, the system also supports a remote monitoring function, so that doctors or family members can know the treatment progress and skin state change in real time through a mobile phone APP and make adjustments in time, and more intimate and comprehensive treatment services are provided for patients.

The invention provides a medical image segmentation method and system based on spatial detail enhanced vision, and relates to the technical field of image processing. The method comprises the following steps: performing initial feature mapping on an input and preprocessed medical image to obtain an embedded feature map; the embedded feature map is input into an encoder for feature extraction, and multi-scale features are obtained; the encoder comprises a plurality of encoding stages, and the number of channels is doubled and the spatial resolution is halved through down-sampling operation between the encoding stages; the multi-scale features are subjected to up-sampling and spatial enhancement reconstruction step by step through a decoder, and a high-precision segmentation result is generated; wherein the decoder comprises a plurality of decoding stages, the decoding stages correspond to the encoding stages, and feature fusion is carried out between the corresponding stages of the encoder and the decoder through jump connection. According to the method, the boundary description precision and the segmentation robustness of the low-contrast image can be improved without increasing the linear complexity, and the method is suitable for medical image segmentation scenes of skin lesions, gastrointestinal polyps and the like.

The invention provides a dermatoscope image segmentation method based on an attention mechanism and multi-scale information interaction, and belongs to the technical field of medical image segmentation. The technical problems that a traditional segmentation method is inaccurate in skin lesion area boundary recognition, insufficient in multi-scale feature extraction capacity and large in noise interference influence are solved. According to the technical scheme, the method comprises the following steps of 1, constructing a high-precision skin disease image data set; step 2, performing enhancement and standardization processing on the data set; 3, constructing a segmentation model based on an attention mechanism and multi-scale information interaction; 4, training the segmentation network by using the preprocessed data set, and optimizing the model performance by using a BCE-Dice combination loss function; and 5, inputting a test set to evaluate the performance of the model, and finally outputting an optimal segmentation result. The method has the advantages of being high in segmentation precision, high in boundary recognition capability and excellent in model robustness.

The invention discloses a fuzzy edge enhanced skin lesion image segmentation method and system. The method comprises the steps of S110-S140: S110: collecting clinical skin lesion image data; s120, constructing a fuzzy edge segmentation network ResPDCNet of the residual error pixel differential convolution; s130, carrying out training on the constructed ResPDCNet (Registered Packet Data Convergence Network And S140, according to a network training result, giving a skin lesion region segmentation result. The invention provides a fuzzy edge enhanced skin lesion image segmentation method based on residual pixel difference convolution. According to the method, detail edge details of a target lesion area are extracted through a residual pixel convolution block, the problem that the pixel difference between a foreground area and a background area in a clinical medical image is small is solved, and then space and channel feature extraction is carried out through a residual attention block to perceive the size and position of a lesion part. The problems of different sizes of lesion parts and difficulty in positioning the lesion positions are solved.

The invention discloses a spirulina platense skin extract as well as a preparation method and application thereof, and relates to the technical field of biomedicine. The obscured spirulina skin extract is rich in various active ingredients including collagen family members, viscose protein, C1Q complement protein, fibronectin and the like, and the ingredients complement and promote one another, so that the extract has remarkable effects in the aspects of skin aging resistance, skin injury repair and the like; therefore, the method has a wide application prospect in medicine research and development, cosmetics and functional foods. According to the extraction method of the skin protein of the obtuse spirulina, the sample is subjected to rapid freeze thawing treatment, and the steps of mechanical crushing, ultrasonic-assisted separation and multiple filtration are combined, so that the influence on the activity of the product is avoided, and various compound proteins can be obtained; the pH value of a buffer solution is adjusted, the property of the extract is further optimized, and the extract is more suitable for human skin, is sterile and can be directly used as a product raw material, so that large-scale production is realized, and industrial application of the obtuse spirulina skin extract is promoted.

The invention provides a U-shaped neural network skin lesion segmentation method and equipment, and relates to the technical field of image segmentation, and the method comprises the steps: S1, obtaining a skin lesion image set, carrying out the preprocessing and data normalization of the skin lesion image set, and obtaining a training data set; s2, constructing a U-shaped neural network, and training the U-shaped neural network through the training data set to obtain a trained U-shaped neural network; and S3, inputting a to-be-segmented skin lesion image into the trained U-shaped neural network to obtain a segmented skin lesion image. According to the U-shaped neural network, a coding and decoding device is composed of a fusion convolution Mangbar module, rich features can be extracted, a better segmentation effect can be achieved for skin lesions with fuzzy boundaries and low contrast, meanwhile, multi-level and multi-scale information fusion can be achieved by combining a space attention module and a channel attention module, and the method is suitable for being applied to skin lesions. And high-precision skin lesion segmentation is realized.

The embodiment of the invention discloses an electronic medical record generation method and device, electronic equipment and a storage medium. The method comprises the following steps: in response to an electronic medical record generation instruction for a skin inquiry service, obtaining interaction content between a provider and an acquirer of the skin inquiry service and a deep learning model for realizing electronic medical record generation; acquiring skin data of the skin lesion part inquired by the acquirer; wherein the skin data comprises at least one of a skin image and a skin medical report; and generating an electronic medical record corresponding to the skin inquiry service according to the deep learning model, the interaction content and the skin data. According to the technical scheme provided by the embodiment of the invention, on the basis of the interactive content, the skin data capable of providing main auxiliary diagnosis information for a doctor is combined, so that the deep learning model can be utilized to process the multi-modal information (namely, the interactive content and the skin data) to generate the electronic medical record; therefore, the generation quality of the electronic medical record for the skin inquiry service is improved.

The invention discloses a feature extraction and recognition method for skin lesion hyperspectral image classification, and belongs to the technical field of image processing, and the method comprises the following steps: S1, carrying out the preprocessing of a hyperspectral image; s2, extracting diversity features of the preprocessed hyperspectral image; s3, the extracted features enter a feature fusion module, and feature fusion is realized through a three-dimensional full convolutional neural network of multi-granularity feature fusion; s4, after feature fusion, lesion region segmentation is carried out in a segmentation module; s5, classifying the segmented features in a classification module; s6, enabling the classified lesion areas to enter a detection module, and improving the detection precision and real-time performance of lesion cells in the microscopic hyperspectral image; and S7, verifying a detection result of the detection module by a clinician according to a pathological result of a pathological expert. The feature extraction and recognition method for skin lesion hyperspectral image classification has higher diagnosis and classification capabilities, and is suitable for various medical image analysis tasks.

The invention relates to a Lactobacillus paracasei LC97, and an application, a product and a method thereof, and belongs to the technical field of microorganisms. The preservation number of the lactobacillus paracasei LC97 for relieving allergy is CGMCC (China General Microbiological Culture Collection Center) NO. 32421. Based on the strain, the invention also provides application and related products of the strain. The LC97 strain has broad-spectrum antibacterial performance, has the functions of enhancing immune function and relieving atopic dermatitis, and can relieve atopic dermatitis by relieving skin lesion, inhibiting inflammation, reducing ear swelling degree and regulating cell factor level. And by reducing the total levels of serum IgE and IL-4, the levels of IFN-gamma and IL-10 are improved, and the levels of cytokines are regulated, so that the immunity is enhanced.

The invention relates to application of cembratrienol in preparation of a medicine for preventing and / or treating psoriasis, and belongs to the technical field of biological medicine. The invention provides an application of cembratrienol in preparation of a medicine for preventing / or treating psoriasis. Researches find that cembratrienol can significantly inhibit secretion of inflammatory factors in a psoriasis-like cell model induced by M5; moreover, research finds that cembratrienol can significantly improve the skin lesion condition of an imiquimod-induced psoriasis mouse model, improve the weight loss degree of the psoriasis mouse model, relieve the pruritus condition of the psoriasis mouse model, improve the skin inflammation of the psoriasis mouse model, repair the skin barrier of the psoriasis mouse model, and improve the curative effect of the psoriasis mouse model. The body immune response of the psoriasis mouse model is reduced. Therefore, cembratrienol has a great application prospect in preparation of the medicine for preventing / treating psoriasis.

The invention discloses a skin lesion area segmentation method based on Mama. The method belongs to the technical field of neural network target detection. The technical problems that an existing image segmentation model cannot consider global information of a medical image, and the segmentation performance is poor for target structures with large texture, shape and size differences among different patients are solved. The method comprises the following steps: S1, carrying out preprocessing and data enhancement operation on a skin lesion image, and dividing a data set; s2, constructing a skin lesion region segmentation model, wherein the segmentation model comprises a patch embedding layer, an encoder, a decoder, a jump connection module, a dynamic feature fusion module and a final projection layer; and S3, training the skin lesion region segmentation model by using the data set, and performing lesion region segmentation on the skin lesion image by using the trained skin lesion region segmentation model to obtain a segmentation result graph of the lesion region.

The invention discloses a nanorana alpine antioxidative peptide and application thereof. The antioxidative peptide is a cyclic peptide composed of 29 amino acids, the amino acid sequence of the antioxidative peptide is as shown in SEQ ID NO.1, and cysteine at the seventh position and cysteine at the twelfth position form an intramolecular disulfide bond. The invention further discloses nucleotide for coding the antioxidative peptide of the nanorana pleskei. The antioxidative peptide has good anti-inflammatory and anti-oxidation effects, can protect plasmid DNA damaged by H2O2, inhibits accumulation of CuSO4 induced zebrafish larva active oxygen and migration of neutrophil, and relieves mouse skin injury induced by UVB radiation.

The invention provides a skin lesion auxiliary detection method and system based on multi-modal information fusion, and belongs to the technical field of computer vision and application. The method comprises the following steps: firstly, acquiring and constructing bimodal data including a dermatoscope image and a text of a patient, and preprocessing the data; secondly, respectively adopting an improved convolutional neural network and a text coding model, and extracting deep and high-dimensional features from the skin disease image and the self-described text of the patient; afterwards, an improved symmetric gating cross attention fusion module is constructed, fine-grained interaction and alignment between visual and text features are realized through a bidirectional cross attention mechanism, and a gating unit is utilized to adaptively adjust a fusion weight; and finally, inputting the deeply fused multi-modal feature vector into a classifier to obtain a confidence score of each skin lesion type so as to provide reference for dermatologists. According to the method, the diagnosis accuracy, robustness and interpretability are synergistically improved, and the method has great clinical application value.

A method of operation of a compute system includes: receiving a patient image including a skin lesion and a fixed ruler, generating a correlated ruler by segmenting the fixed ruler including translating pixel-to-millimeter to the patient image, calculating a skin lesion area by segmenting the skin lesion, and generating a skin lesion output report, including an image of the skin lesion, the correlated ruler and the skin lesion area, for displaying on a device.

The invention relates to the technical field of medical image segmentation, and discloses a model for enhancing medical image segmentation through multi-scale and multi-view frequency fusion, the model is named as MSFM-UNet, and the MSFM-UNet comprises a VSS module, a layer used for combining patches, a layer used for expanding the patches and a module special for multi-scale feature aggregation (MSFA). According to the invention, accurate segmentation is realized in a medical image by using multi-scale feature aggregation (MSFA) and multi-view frequency enhancement (MVFE). A proposed MSFM-UNet model comprises a core VSS module, features of different encoder levels are integrated by using MSFA, feature representation is enriched by simultaneously capturing global frequency domain signals and local spatial information by applying MVFE, and comprehensive evaluation of skin lesions and multi-organ segmentation data sets shows that the feature representation is enriched by using the MSFA and the MVFE. The model provided by the invention shows strong performance in the field of medical image segmentation tasks, and solves the problems of difficulty and limitation when long-distance correlation is captured.

This invention is a method, device, or system to guide a person concerning how to operate a conventional phone to record more-accurate images of a pathology or injury (e.g. such as a wound or skin lesion) on a body for remote medical evaluation purposes. In an example, machine learning and / or artificial intelligence can be used to guide the person concerning how to move a phone along a virtual arcuate path or shape in space over, across, or around the pathology or injury. In an example, this guidance can be auditory, visual, and / or tactile.

The invention relates to application of VBIT-4 or pharmaceutically acceptable salt thereof in preparation of drugs for preventing or treating vitiligo and complications thereof. A large number of modern pharmacological scientific researches and in-vivo pharmacodynamic tests prove that VBIT-4 has a treatment effect on a leucoderma mouse model and inhibits synthesis of inflammatory factors and chemotactic factors in a leucoderma mouse skin lesion area. The mechanism of the invention is that VBIT-4 inhibits the expression of inflammatory factors and chemotactic factors in skin tissues so as to treat leucoderma, compared with the existing treatment means, VBIT-4 can promote the compound color of pigments in a skin lesion region on the back of a leucoderma mouse, has no obvious toxicity, and reduces the expression level of inflammatory factors (IL-1beta, IL-6 and IFN-alpha) and chemotactic factors (CXCL9 and CXCL10). In conclusion, the VBIT-4 is clear in action mechanism, remarkable in effect and free of obvious side effects, and has remarkable advantages and progress compared with existing drugs.

The invention belongs to the technical field of image recognition, and particularly relates to a skin disease classification recognition system based on deep learning, which comprises a skin image acquisition module, a skin image preprocessing module, a skin state classification module and a state classification optimization module, according to the system, a skin state recognition model is constructed, and a multi-head self-attention mechanism is introduced, so that skin state differences can be distinguished; local features are extracted through a space exploration CNN block, an AssemFormer block learns a global dependency relationship, the local features and the global features are fused, and a lesion mode can be understood; scale variant attention blocks are used for highlighting skin features of suspected lesion areas, Monte Carlo attention blocks are used for carrying out random sampling pooling calculation on feature representation, attention map enhancement blocks are used for generating attention maps, skin state categories are output, and skin state prediction labels are generated for each skin image, so that skin lesion details can be highlighted; quantitative reference is provided for medical staff, and skin state judgment is effectively assisted.

The invention relates to application of bifidobacterium breve BBr60 in preparation of a probiotic preparation for improving atopic dermatitis. The bifidobacterium breve BBr60 is a bifidobacterium breve BBr60 strain of which the preservation number is CGMCC (China General Microbiological Culture Collection Center) No.12915, and the bifidobacterium breve BBr60 strain of which the preservation number is CGMCC No.12915. The invention develops a new application of the bifidobacterium breve BBr60, provides a research basis for wide application of the bifidobacterium breve BBr60, also develops a brand new strategy for improving atopic dermatitis, discovers that the bifidobacterium breve BBr60 has a very remarkable effect on improving atopic dermatitis, and has a very good application prospect. Specifically, the skin lesion phenomenon and the mast cell inflammation infiltration phenomenon in the skin of an atopic dermatitis model mouse are remarkably improved; the IgE level in serum of an atopic dermatitis model mouse is remarkably reduced and improved, the levels of IFN-gamma and IL-10 are improved, and Th1 / Th2 immune balance is recovered; the intestinal flora uniformity of atopic dermatitis model mice is recovered, and the relative abundance and F / B ratio of bifidobacterium and lactobacillus in the intestinal flora of the mice are remarkably improved.

The invention provides a skin lesion image segmentation method and system based on multi-scale deformable aggregation, and the method comprises the steps: obtaining a skin lesion image data set, dividing the data set into a training set and a test set, constructing an improved U-shaped segmentation network model, optimizing model parameters through training, applying the trained model to the test set, and obtaining a skin lesion image segmentation result. And generating a high-precision segmented image. According to the model, an innovative convolution-parallel vision Mangbar module is adopted in an encoder, and extraction and fusion of local details and global long-range dependency of an image are realized through cooperation of a CNN and a state space model; a deformable convolution attention gate is put forward in a decoder, a key area is accurately focused by using a deformable convolution adaptive lesion shape, and cross-level feature fusion and multi-scale refining are realized. Through the multi-scale deformable aggregation mechanism, the segmentation precision and robustness of the model to complex and various skin lesions are significantly improved, and reliable assistance is provided for clinical diagnosis.

The invention relates to the technical field of skin lesion area reconstruction, in particular to a skin lesion area reconstruction system based on multi-modal image fusion. The system comprises a deep multi-scale feature extraction module, a skin thermal anomaly feature extraction module, a hemodynamic feature extraction module, a depth and boundary feature extraction module, a skin surface feature extraction module, a fluorescence intensity distribution and morphological feature extraction module, a first feature fusion module, a second feature fusion module and a lesion area reconstruction module. According to the method, appearance, heat, blood flow, structure and surface features are extracted from the multi-modal data, then the multi-modal image data are integrated, finally, comprehensive representation and reconstruction of skin lesion appearance, function and structure information are achieved according to the synergistic effect of the multi-modal information, and the accuracy and clinical reference value of lesion area reconstruction are effectively improved.

The invention provides a multi-enhancement semi-supervised medical image classification method based on a dynamic threshold value, and the method comprises the steps: obtaining a labeled image and a non-labeled image based on a skin disease image data set, building an initial semi-supervised image classification model through the labeled image and the non-labeled image based on deep learning, and carrying out the classification of the initial semi-supervised image. Respectively processing and merging the images with the labels and the images without the labels, obtaining a loss function based on a merging result, optimizing the initial semi-supervised image classification model based on the loss function to obtain a target semi-supervised image classification model, and evaluating and adjusting the target semi-supervised image classification model based on a performance evaluation index to obtain a target semi-supervised image classification model. The accuracy and generalization ability of the skin lesion image classification model are improved, and the dependence on large-scale manual annotation data is reduced by designing an effective learning mechanism, so that the annotation cost is reduced, the training efficiency is improved, the robustness of the model to the image data distribution change is enhanced, and the stable and consistent classification performance is kept.

The invention relates to a skin lesion segmentation method based on error positioning and adaptive optimization, and the method comprises the steps: dividing a skin lesion image shot by a dermatoscope and a corresponding segmentation label into a training set, a verification set and a test set; then constructing a segmentation network, training the constructed segmentation network by adopting a training set, continuously monitoring the generalization ability of the segmentation network through a verification set in the training process, and finally obtaining a trained segmentation network; and finally, inputting the test set into the trained segmentation network, and outputting a segmentation result of the skin lesion. The skin lesion segmentation method based on error localization and adaptive optimization effectively focuses on a key area needing to be optimized in actual training, and helps the model to more accurately understand and distinguish the features of lesion and normal skin. Therefore, the segmentation capability of the method in processing complex lesions such as fuzzy boundaries, complex shapes and background noise is improved.

The invention relates to the technical field of medical artificial intelligence and computer vision, in particular to a skin disease feature recognition method based on image processing, and the method comprises the following steps: S1, obtaining multi-modal data of a skin lesion region, including an RGB image, a thermal imaging image and a patient medical history text; s2, performing illumination correction, noise suppression and lesion region segmentation on the image data; the skin disease feature recognition system based on image processing comprises the following contents: a multispectral image acquisition module supporting synchronous acquisition of visible light, infrared and depth data; in the using process of the method and system, the diagnosis efficiency and accuracy of medical staff can be improved, the medical staff can be effectively assisted to analyze and confirm the type of the illness state, and meanwhile the doctor seeing efficiency of the medical staff can be effectively improved.

The invention relates to the technical field of auxiliary medical treatment, and discloses a foot disease treatment method and device based on image recognition, and the method comprises the steps: obtaining a to-be-analyzed foot image; identifying a skin lesion area in the foot image to be analyzed, and determining skin lesion features of the skin lesion area; determining a skin lesion type based on the skin lesion features; skin lesion monitoring data and current parameters of an air bag control system are obtained, the air bag control system comprises an air bag array, the air bag array is used for treating the foot, and the parameters comprise the pressure, the temperature and the humidity of each air bag; determining treatment data based on the skin lesion type, the skin lesion monitoring data and the current parameters; the treatment data are converted into control signals to be transmitted to the air bag control system, and the air bag control system is used for adjusting parameters of each air bag according to the received control signals. The method is simple in implementation mode, does not need to depend on professional knowledge and diagnosis experience of medical staff, and can effectively improve the treatment experience feeling of a patient.

The invention relates to the technical field of medical devices, and provides an accurately adjustable shading device for dermatological herpes zoster phototherapy, the shading device comprises a support plate, an adjusting assembly, an efficient assembly and a sanitary assembly, the bottom of the support plate is fixedly connected with two symmetrically arranged support legs, and through the arrangement of structures such as a threaded rod and an adjusting rod, the shading device can be used for the dermatological herpes zoster phototherapy. Through the arrangement of the threaded rod, an elastic sleeve and other assemblies can be driven to integrally move in the horizontal direction, the position of the skin lesion of the human body can be directly positioned to replace a handheld treatment mode, and after basic positioning, each adjusting rod can be independently rotated according to the specific shape of the skin lesion range; when each adjusting rod rotates, the shape of the elastic sleeve can be driven to change until the elastic sleeve is adjusted to be roughly the same as the skin lesion range, finally, the elastic sleeve is attached to the skin through the electric control air cylinder, the adjustable effect is achieved, and through the technical scheme, the problems that in the prior art, shading cannot be adjusted, and the treatment effect is poor are solved.

The invention provides an image-based skin treatment effect evaluation method, medium and system, and belongs to the technical field of skin treatment effect evaluation.A standardized image is collected, a focus area is recognized by applying a skin spectral analysis model, a three-level focus area division model is constructed, and a current boundary and a prediction boundary are calculated; determining a boundary transition index; pixel-level pigment analysis is carried out on the third-level focus area, a pigment distribution matrix is constructed, a pigment concentration representative center is extracted, and pigment distribution skewness is calculated; establishing a time sequence image library for comparison before and after, and calculating a focus area change rate, a boundary transition index change rate and a pigment concentration change rate; combining the indexes to calculate a comprehensive treatment effect score, and generating a thermodynamic diagram to display the treatment progress; a patient individualized evaluation model is established, evaluation precision and an individualized treatment scheme are optimized, and the technical problems that in the prior art, evaluation objectivity of the skin lesion treatment effect is insufficient, and accurate quantification is difficult are solved.

The invention relates to a medical image segmentation system and method based on context and frequency guidance and a storage medium, and the system employs an encoder-decoder architecture, and integrates three core modules: a visual state space module which captures global context information with linear complexity based on a selective state space model; the frequency-guided representation module is used for explicitly separating and enhancing structure and boundary characteristics through frequency domain transformation and complex weight modulation; and the multi-scale adaptive context aggregation module is used for integrating multi-scale semantics through parallel multi-branch convolution and dual Top-K sparse attention and focusing a key region. According to the method, the technical problems that global modeling and boundary details are difficult to consider, the calculation complexity is high and the adaptability to multi-scale targets is poor in the prior art are solved, the segmentation precision and the boundary description accuracy are remarkably improved in various medical image segmentation tasks such as heart MRI, polyp, skin lesion and pathological sections, and the medical image segmentation efficiency is improved. And meanwhile, the calculation efficiency is ensured.