67 results about "Breast tumor" patented technology

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention relates to a breast tumor lesion part ultrasonic image segmentation method based on an SAM-VMama model. The method comprises the steps of obtaining a breast ultrasonic image set and performing preprocessing; the method comprises the following steps: constructing an SAM-VMama model, wherein the SAM-VMama model comprises a first encoder, a second encoder, a prompt encoder and a mask decoder; obtaining a trained model; obtaining a preprocessed breast ultrasound image to be segmented; and obtaining a segmentation result of the breast lesion tumor. According to the method, the local features are extracted through the first extraction, the global features are extracted through the second encoder, the interactive prompt information processed by the prompt encoder is combined, and the precise segmentation mask is generated through the mask decoder; the method not only retains the flexibility of SAM interactive segmentation, but also improves the long-sequence data processing capability and calculation efficiency through a Mamba structure, effectively adapts to the characteristics of uneven gray scale, multiple artifacts and the like of the ultrasonic image, and achieves the balance of high precision and high efficiency.

The invention belongs to the technical field of microbial fermentation, and particularly relates to a lactobacillus rhamnosus fermentation composition, a preparation method and application of the lactobacillus rhamnosus fermentation composition in the aspects of improving the tumor cell induced inflammation level and preventing and inhibiting breast tumors. The invention provides a lactobacillus rhamnosus fermentation composition. The lactobacillus rhamnosus fermentation composition is prepared from a raw material and lactobacillus rhamnosus NKU ML1-2, the raw materials comprise raspberry, poria cocos, cape jasmine, rehmannia, liquorice and orange peel. The lactobacillus rhamnosus fermentation composition has the effects of nourishing yin and clearing heat, soothing liver and regulating qi, and strengthening spleen and stomach. Results of embodiments show that the lactobacillus rhamnosus fermentation composition can prevent proliferation, invasion and migration of breast cancer tumor cells to a certain extent, and has an improvement effect on the level of inflammation induced by the tumor cells.

The invention discloses a mammary gland ultrasonic video tumor segmentation method and device based on spatial-temporal feature fusion, and aims to realize accurate mammary gland tumor segmentation. The method comprises the following steps: S1, preprocessing breast ultrasound video data, dividing the data into a training set, a verification set and a test set, and processing the data set into breast ultrasound continuous frames; s2, extracting initial features of the breast ultrasonic continuous frames through down-sampling; s3, constructing a Transform module, and extracting global features of the breast ultrasonic video data; s4, constructing a spatial feature extraction module, and extracting spatial features of the initial features; s5, constructing a time feature extraction module, and extracting time features of the initial features; s6, constructing a multi-scale feature fusion module, and performing feature fusion on the time features, the spatial features and the global features to obtain final features; and S7, performing up-sampling on the final features to obtain a breast tumor segmentation map. The method can effectively improve the segmentation precision of the breast tumor, and can be used for tumor segmentation of the breast ultrasonic video.

This invention discloses a medical image segmentation method based on inverse residual attention and multi-scale context. The method inputs the medical image to be segmented into an improved RAS-UNet segmentation network model. This model introduces an inverse residual attention module at the bottleneck feature of the deepest layer of the encoder, which suppresses background through an inverse weighting mechanism and enhances attention to blurred boundaries and weak response regions. Simultaneously, a hollow spatial pyramid pooling module is introduced into the decoding path, utilizing a parallel multi-branch structure to fuse multi-scale contextual information to adapt to lesions or anatomical structures of different sizes. Skip connections between the encoder and decoder achieve efficient fusion of detailed and semantic features. This invention can significantly improve the accuracy and boundary accuracy of segmenting medical targets such as polyps, breast tumors, and the hippocampus, providing a reliable tool for clinical auxiliary diagnosis.

The invention discloses a medical image segmentation method and system based on SAM fine tuning and automatic prompt generation, and belongs to the field of medical image processing, and the method comprises the steps: employing a pixel-level feature adapter to enhance the feature representation of a medical image, and then employing low-rank adaptation LoRA to carry out the efficient fine tuning of parameters of an SAM image encoder; secondly, an automatic bounding box generator based on a Transform decoder is designed, and a target bounding box is directly regressed from image features to serve as a space prompt; and finally, constructing a two-stage mask decoder, in the first stage, generating a coarse segmentation mask by using the frozen native SAM decoder in combination with the bounding box prompt, and in the second stage, inputting the coarse mask as an additional prompt into a trainable decoder for fine segmentation, and outputting a final result. According to the method, full-automatic and high-precision medical image segmentation is realized, and the segmentation performance on various medical images such as skin lesions, breast tumors and lung nodules is remarkably improved while the original architecture advantages of the SAM are reserved.

This invention relates to a method for precise localization of breast tumors based on multi-level double-layer coding, comprising the following steps: acquisition of scattered microwave signals; signal processing and feature construction; multi-level data label coding based on a breast model containing the tumor target: each level contains double-layer coding, the first layer of coding is coarse-grained localization, and the second layer of coding is fine-grained localization; the localization interval of the initial level is determined based on the breast model containing the tumor target; given the constraints of the termination coding, the localization interval of the next level is determined based on the current level's localization interval after data label double-layer coding; model training and coding prediction output.

The invention provides a breast cancer ultrasonic image segmentation method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining an original breast ultrasonic image; performing data enhancement processing on the original breast ultrasound image; a breast tumor segmentation model is constructed, and the breast tumor segmentation model comprises an MGM encoder module, a multi-scale cross attention module, an edge feature refining module and an EMCAD decoder module which are connected in sequence; inputting the original breast ultrasound image after data enhancement processing into the breast tumor segmentation model, and training the breast tumor segmentation model; acquiring a real-time breast ultrasonic image; and inputting the real-time breast ultrasonic image into the trained breast tumor segmentation model, and outputting a final segmented image. The method depends on a multi-module collaborative architecture, fuses multi-scale semantic features, accurately refines tumor edges, and greatly improves tumor segmentation precision and edge positioning accuracy.

This invention discloses a medical image segmentation method based on enhanced attention. The method acquires a medical image, preprocesses it, and inputs it into an encoder to output a multi-scale feature map of the medical image. The encoder includes N cascaded multi-dimensional attention modules. Each multi-dimensional attention module extracts multi-scale features through dual-channel convolution and residual connections, and performs feature recalibration using an attention enhancement module. The output multi-scale feature map is then fused by a multi-scale dilated fusion attention module to obtain the feature map output by the encoder. The feature map output by the encoder is input to the decoder, where it is combined with high-resolution feature maps of the corresponding levels passed from the encoder via skip connections. Through progressive upsampling and feature fusion, a reconstructed feature map is obtained. The reconstructed feature map output by the decoder is used to generate a medical image segmentation mask through 1×1 convolution and a sigmoid activation function to complete the segmentation of diseased tissues. This invention significantly improves the boundary segmentation accuracy for knee joints, breast tumors, and skin lesions.

The application discloses a low-temperature freezing rotary cutting device and a rotary cutting system for breast tumor diagnosis. The outer tube outer layer and the outer tube inner layer are arranged on the fixing member, and a sampling groove is formed by the slot opening and the sealing structure. The inner tube and the inner cutting blade are arranged in the outer tube inner layer. The sealed cavity formed between the outer tube outer layer and the outer tube inner layer is divided into an expansion cavity and a backflow cavity. The air inlet structure is arranged to extend into the expansion cavity to output the refrigerant. The outer tube outer layer is provided with an insulating material layer to isolate the cold energy transmission. The tumor tissue is sucked into the sampling groove under negative pressure. The refrigerant is released into the expansion cavity through the air inlet structure to expand and cool. The cold energy is transmitted to the sampling groove through the outer tube inner layer to freeze the tumor tissue, so that the original biological elasticity is lost. Then, the tissue is rotary cut by the inner cutting blade on the inner tube, and the cutting efficiency and the sampling efficiency are improved.

Provided herein is a method for detecting a tumour that can be applied to cell-free samples, to cell-free detect circulating tumour DNA. The method utilizes detection of adjacent methylation signals within a single sequencing read as the basic positive tumour signal, thereby decreasing false positives. The method comprises extracting DNA from a cell-free sample obtained from a subject, bisulphite converting the DNA, amplifying regions methylated in cancer (CpG islands, CpG shores, and / or CpG shelves), generating sequencing reads, and detecting tumour signals. To increase sensitivity, biased primers designed based on bisulphite converted methylated sequences can be used. Target methylated regions can be selected from a pre-validated set according to the specific aim of the test. Absolute number, proportion, and / or distribution of tumour signals may be used for tumour detection or classification. The method is also useful in, predicting, prognosticating, and / or monitoring response to treatment, tumour load, relapse, cancer development, or risk.

The application relates to the technical field of nursing, in particular to a breast tumor postoperative nursing belt which comprises two protective belts, an inflation mechanism is arranged between the two protective belts, the lower edge of each protective belt is connected with an extension belt, the front surface of the extension belt is fixedly connected with a soft belt, the upper edge of the front surface of the soft belt is fixedly connected with a hard plate, a sliding opening is arranged in the front surface of the hard plate, a connecting seat is slidably connected to the inner side of the sliding opening, an elastic sheet is arranged at the rear end of the connecting seat, pulling mechanisms are arranged at the two sides of the front surface of the elastic sheet, and a binding mechanism is arranged at the side away from each other of the two protective belts. The drainage tube is led out from the rear of the elastic sheet, the problem that the drainage tube is squeezed can be avoided, the drainage is ensured, when the protective belt is inflated, the rear end of the elastic sheet is well limited, and the elastic sheet is prevented from being easily slid and displaced in the later use.

The invention discloses a breast ultrasound image segmentation method and device based on semantic perception, and the method comprises the steps: obtaining an ultrasound image of a patient, constructing a semantic perception breast tumor segmentation network of the ultrasound image, employing an encoder to capture the global structure information of the ultrasound image, employing a semantic perception block to extract low-level features from the global structure information, and carrying out the segmentation of a breast tumor. A plurality of decoders combine the global structure information with the low-level features to generate an optimized breast tumor segmentation result, and the decoders combine the global structure information extracted by the encoders with the low-level detail features acquired by the semantic perception module to finally generate an optimized segmentation result. An output result is ensured to have context accuracy and keep boundary accuracy, the network adopts an efficient U-shaped network architecture and fuses deep convolution and jump connection, different scale changes can be flexibly coped with, meanwhile, additional calculation overhead is avoided, and the method is suitable for large-scale application. Therefore, the effectiveness and robustness of breast tumor segmentation of the breast ultrasound image are improved.

The invention relates to the technical field of medical pathological detection auxiliary equipment, in particular to a breast tumor specimen cutting and sampling device. The breast tumor specimen cutting and sampling device provided by the invention can automatically cut and sample a breast tumor specimen, saves manpower, and improves the specimen processing efficiency and safety. A breast tumor specimen cutting and sampling device comprises a sterile box, a sealing door and the like, and the front side of the left portion of the sterile box is rotationally connected with the sealing door. A micro motor is started to drive a saw blade to rotate, so that the saw blade cuts a breast tumor specimen, while cutting, a suction pump is started to suck breast tumor specimen chippings generated by cutting into a storage bin through a connecting pipe, and a sample storage box collects the chippings, so that the breast tumor specimen can be automatically cut and sampled; manpower is saved, and the specimen processing efficiency and safety are improved.

The invention discloses a breast cancer treatment scheme decision-making method fusing agent cooperation and game theory modeling, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: inputting a multi-sequence MRI image into an expert network, and obtaining a molecular typing result; according to a molecular typing result, a diagnosis and treatment guide and clinical text information of the patient, dividing into corresponding risk grades; adopting a Stackelberg game model to formulate a treatment scheme corresponding to the risk level, and for the low risk level LRT, formulating a treatment scheme through a single expert model; aiming at the middle risk level MRT, making a treatment scheme by adopting a prison dilemma game model; and aiming at the high-risk-level HRT, a treatment scheme is formulated by adopting a hunting deer game model. According to the method, the accuracy and reliability of breast tumor diagnosis decision are improved by combining the multi-modal patient information, the three-layer risk level, the large language model and the game theory driven multi-agent cooperation.

The invention discloses a breast cancer heterogeneous component automatic segmentation and quantitative analysis method and system. The method comprises the following steps: S1, collecting multi-temporal breast DCE-MRI original image data; step S2, carrying out comprehensive preprocessing on the DCE-MRI original image data; s3, based on a deep learning technology, automatically segmenting the breast tumor and internal heterogeneous components to obtain a segmentation result; s4, based on the segmentation result, automatically generating a time-signal intensity curve and a contrast agent concentration curve; s5, fitting the generated standardized time-signal intensity curve and the contrast agent concentration curve, extracting features, and classifying the features into different dynamic enhancement modes; s6, key quantitative parameters are extracted from the standardized time-signal intensity curve, and pharmacokinetic characteristics are extracted from the contrast agent concentration curve; and S7, establishing a comprehensive analysis model. Through deep learning and image processing technologies, an objective basis is provided for accurate diagnosis, molecular typing prediction and individualized treatment of breast cancer.

The utility model relates to the technical field of rehabilitation exercise, and discloses a breast tumor nursing rehabilitation exercise device which comprises a base, a vertical plate is fixedly installed on the upper surface of the base, a top plate is movably connected to the inner wall of the top of the vertical plate, and a connecting rope is fixedly connected to the lower surface of the top plate. The bottom end of the connecting rope is fixedly connected with a pull ring through an elastic rope, and a protection assembly is arranged on the lower surface of the top plate. Through the arrangement of the protection assembly, when the pull ring slips out of the hand when a patient breaks down or gets out of force, the elastic rope is rapidly tightened, the transverse deviation trend of the elastic rope is restrained through rotation of a shifting block, and meanwhile through limitation of a connecting spring and an arc-shaped block, the elastic rope can be prevented from slipping out of the hand. Therefore, rotation possibly generated due to contraction of the elastic rope can also restrain rotating force generated by the shifting block and the baffle ring in the horizontal direction, and therefore the elastic rope cannot generate movement with the large amplitude in the horizontal direction in the rapid contraction process.

The utility model relates to medical equipment technical field discloses a kind of multi-angle breast tumor image linkage observation stand, including stand, the upper end of the stand is connected with telescopic column for adjusting, the side of telescopic column is fixedly installed with the mounting plate for connection, the side of the mounting plate is fixedly connected with the support for positioning.This multi-angle breast tumor image linkage observation stand is cooperatively arranged by stand, telescopic column, mounting plate, support, first adjusting plate, second adjusting plate, connecting frame and display screen, the angle adjustment of display screen in horizontal rotation can be realized by the combination of first adjusting plate and second adjusting plate, the detachable design of connecting frame is combined, to form all-around adjustment range, thereby solve the limitation of the fixed angle of traditional observation stand, doctor can observe tumor morphology, boundary and the relationship with surrounding tissue from different angles without repeatedly adjusting own posture, avoid missing key information due to insufficient viewing angle.

The invention discloses a pharmacokinetic parameter estimation method, device and system and a storage medium. The pharmacokinetic parameter estimation method comprises the steps that S1, an obtained breast tumor data sample is preprocessed; s2, according to the preprocessed breast tumor data sample, constructing and training a space-time separation fusion model oriented to DCE-MRI data; and S3, inputting to-be-processed breast tumor data into the trained time-space separation fusion model to obtain a pharmacokinetic parameter estimation value. By adopting the technical scheme provided by the invention, the PK parameter of the breast tumor can be accurately estimated.

This invention relates to the field of computer-aided diagnosis, and in particular to a breast tumor identification system based on multimodal ultrasound imaging. The breast tumor identification system provided by this invention performs the following steps: acquiring a first ultrasound modality breast image, and locating a suspected first breast tumor region in the first ultrasound modality breast image; acquiring a second ultrasound modality breast image, and aligning the second ultrasound modality breast image with the first ultrasound modality breast image; based on the alignment result, locating a second suspected breast tumor region in the second ultrasound modality breast image corresponding to the suspected first breast tumor region; and identifying the confidence level of the breast tumor in the suspected second breast tumor region based on the distribution of microvessels in the suspected second breast tumor region. The breast tumor identification system based on multimodal ultrasound imaging provided by this invention, combining breast images from both the first and second ultrasound modalities, achieves more accurate breast tumor identification and localization, improving detection accuracy.

Breast cancer is now the most prevalent cancer worldwide, and despite therapeutical advances in the last decades, metastatic breast cancer remains an incurable disease. Novel tumor-specific antigens (TSAs) and tumor-associated antigens (TAAs) expressed by breast tumor cells are described herein. Synthetic long peptides, nucleic acids, compositions, cells, TCRs, antibodies and vaccines derived from these TSAs and TAAs are described. The use of the TSAs / TAAs, nucleic acids, compositions, antibodies, cells and vaccines for the prevention or treatment of breast cancer, including triple-negative breast cancer (TNBC), is also described.

Provided are a method for breast tumor breast imaging reporting and data system (BI-RADS) classification based on ultrasound and a computer device. The method includes: processing an ultrasound radio frequency (RF) signal of a breast tumor to obtain a lesion location RF signal; processing the lesion location RF signal based on a feature extraction model to obtain a feature pixel matrix; and identifying and classifying the feature pixel matrix based on a preset strategy to obtain a class of the breast tumor. The method for breast tumor BI-RADS classification based on ultrasound provided herein can identify the ultrasound RF signal standardly using more efficient deep learning and intelligent classification and identification algorithms, retain more effective information, and finally obtain an identification result by imitating a method of scoring by a clinician based on signal features during identification and constraining each scoring result using a joint loss function.

The application provides a near-infrared two-region fluorescent dye with mitochondrion targeting and aggregation-induced emission properties and application thereof in biological fluorescence imaging and tumor treatment. 41 H 37 F6N2PS, which has good light stability, outstanding mitochondrion targeting, clear ROS production mechanism and excellent photothermal performance. The substance has good mitochondrion targeting capacity and can destroy tumor mitochondria under laser irradiation of a certain intensity, thereby killing tumor cells. The aggregation-induced emission property makes it different from traditional aggregation quenching emission materials and has better biological imaging effect. The near-infrared property makes it have good tissue penetration capacity and biological fluorescence imaging effect. The substance has wide application prospects in near-infrared fluorescence imaging, preparation of mitochondrion targeting reagents and photodynamic therapy reagents. It can be applied to combined phototherapy of breast tumor PDT and PTT, and has simple synthesis steps and is conducive to commercial application and popularization.

This invention provides an AI-assisted diagnostic method and system for breast tumors. Through innovative hardware and software integration design, advanced multimodal fusion AI algorithms, and unique optimization strategies based on pathological knowledge, it systematically solves the core pain points of existing technologies, such as strong subjectivity, low efficiency, and poor consistency. It provides a complete and advanced technical path for achieving accurate, efficient, and standardized ultrasound diagnosis of breast tumors.

This invention discloses a method for breast ultrasound tumor detection based on YoLoV8, comprising the following steps: S1, acquiring breast ultrasound images and performing data preprocessing; S2, extracting features from the preprocessed breast ultrasound images to obtain multi-scale features; S3, designing an improved lightweight HoVer-Trans block, inputting the multi-scale features into the lightweight HoVer-Trans block for lightweight processing; S4, using a lightweight convolutional fusion module to perform feature fusion on the lightweight processed multi-scale features, performing diagnostic classification based on the fused global features, and outputting the diagnostic results and corresponding confidence levels. This invention fully utilizes the global information of ultrasound images, improving the accuracy and comprehensiveness of diagnosis, and meeting the needs of real-time clinical diagnosis.

The application discloses an iridium complex sound sensitizer, a preparation method and application thereof, and belongs to the technical field of anti-tumor sound sensitizer drugs. The iridium complex sound sensitizer has two structures, which are named as Ir-1 and Ir-2 respectively, and the preparation methods of the iridium complex sound sensitizer with the two structures are provided. The preparation method is simple, raw materials are easy to obtain, and purification is convenient and easy to operate. Moreover, the prepared iridium complex sound sensitizer has excellent optical physical properties, exhibits good therapeutic effect in mouse breast tumors, effectively inhibits the growth of tumors, and has excellent sonodynamic therapy effect.

The present specification discloses a composition comprising 17-AAG and staurosporin for use in the treatment of melanoma, non-small cell lung cancer, pancreatic cancer, mastadenoma or glioma, and a stable polymer-lipid nanoparticle for drug binding and delivery, the nanoparticle consisting of a core and a core envelope, the core contains a poly (lactic acid-glycolic acid) copolymer (PLGA) and polyvinyl alcohol (PVA), and the core coating film contains a mixture of the following lipids: 1, 2-dipalmitoyl-sn-glycerol-3-phosphorylcholine (DDPC), cholesterol and 1, 2-distearoyl-sn-glycerol-3-phosphoethanolamine-N-[amino (polyethylene glycol)-2000] ammonium salt (DSPE-PEG (2000) NH2).

1. The name of the design product: breast tumor surgery support cover. 2. The use of the design product: used as medical equipment for supporting and fixing the chest of patients during breast tumor surgery. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: design 1 perspective view 1. 5. Design 1 is designated as the basic design.

The application relates to the technical field of disease auxiliary diagnosis, in particular to a digital breast artificial intelligence auxiliary diagnosis system based on multi-modal fusion, which comprises the following steps: a multi-modal data acquisition module is used for acquiring digital breast image data and rehabilitation scheme data of a patient; an image quality correction module is used for performing acquisition quality evaluation and correction on the digital breast image data; a feature modeling and evaluation module is used for performing feature extraction on the corrected digital breast image data and the rehabilitation scheme data, including an image-drug action mechanism cooperation unit and an image-rehabilitation double-modal co-learning unit; and a rehabilitation effect prediction module is used for constructing a rehabilitation effect prediction model and outputting a rehabilitation prediction result under the influence of a scheme-specific scheme for the current rehabilitation scheme of the patient. The digital breast image and the patient rehabilitation scheme are deeply fused, and the accuracy and reliability of breast tumor rehabilitation prediction are effectively improved.

A breast tumor segmentation method based on multi-modal ultrasound images is provided to solve the problems that the existing breast ultrasound image segmentation technology is prone to misidentifying artifacts and low echo regions as tumors and the tumor boundary segmentation precision is low, comprising the following steps: 1, acquiring paired B-mode ultrasound images and strain elasticity images, and constructing a multi-modal breast ultrasound image dataset; 2, building a multi-modal image segmentation model algorithm based on feature correction alignment and boundary uncertainty enhancement; 3, training the multi-modal image segmentation model based on feature correction alignment and boundary uncertainty enhancement in a five-fold cross-validation manner, and saving the optimal model parameters; 4, loading the optimal model parameters, performing inference prediction on the images in the validation set, calculating the evaluation indexes on the validation set, and comparing with the current other advanced algorithms. The present application can effectively improve the segmentation precision of breast tumors in ultrasound images and improve the accuracy of network prediction. The present application belongs to the field of computer vision, deep learning and medical image segmentation.