2383 results about "Staining" patented technology

The invention discloses a pathological section virtual staining image generation method based on a diffusion model, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, passing through Hamp; e dyeing serves as a source image data set, and pathological section images subjected to IHC dyeing serve as a target image data set; preprocessing the source image, and converting the source image into a corresponding YUV color space; extracting a saliency map and a gradient map of the source image, and generating a weighted attention map; building a diffusion model by using a neural network, and inputting the preprocessed source image for fine tuning; repeatedly iterating until the loss function converges, and ending the training; and inputting a weighted attention map to perform reasoning of virtual dyeing. According to the method, weighted attention of a pathological section image is added in a reasoning part to serve as an image prompt input network, so that the cell structure of a finally generated image is controlled not to be changed, and meanwhile, color features of a target dyed image are reserved and emphasized; and the accuracy and the reconstruction effect of the generated virtual dyeing image are improved.

A colorectal lesion multi-modal classification method based on pathological attention and multi-instance learning constructs an efficient automatic diagnosis model by fusing visual features and a textual prototype defined by pathology experts. The method comprises the following steps: collecting histopathological image data, segmenting the histopathological image data into standardized image blocks, and extracting visual features by using a pre-training feature extraction network after color standardization and noise processing; a multi-instance learning framework and a pathological attention mechanism are combined, feature space distribution of a text prototype is adjusted in a self-adaptive mode through a dynamic prototype optimization module, and optimization targets of visual clustering and cross-modal semantic alignment are balanced by adopting a gradient perception double-loss dynamic weighting strategy; and after the model is trained in stages, the generalization performance is verified in an external data set. According to the method, the classification precision is remarkably improved, the method can adapt to dyeing difference and tissue heterogeneity without pixel-level labeling, the accuracy rate in cross-center verification is superior to that of an existing reference model, and the efficiency of pathological diagnosis is greatly improved.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

The invention relates to the technical field of biomedical detection, and discloses a non-invasive sperm cell death and viability detection method, which utilizes a bright field image and a fluorescence image to train a fluorescence-assisted bright field attention network, introduces a fluorescence attention guidance module to provide guidance for training of a Transformer encoder, and realizes non-invasive sperm cell death and viability detection through priori knowledge of the fluorescence image. By means of the method, the model can be quickly focused on key features displayed in the fluorescent highlight area, the situation that training resources are consumed on a large amount of irrelevant information is avoided, convergence is accelerated, the model training time is shortened, meanwhile, the detection precision is improved, and then a more accurate and efficient sperm cell death and activity detection model is obtained. And during actual detection, the sperm cell death and viability detection can be realized only by collecting a microscopic image, pre-processing the microscopic image and inputting the preprocessed microscopic image into the model without dyeing a sperm sample, so that the damage of a traditional detection method to sperms is avoided, the non-invasive detection of the sperm cell death and viability is realized, and the method is more suitable for clinical detection.

The invention provides automatic dyeing equipment for biological tissue sample slices, which comprises a feeding mechanism, a feeding and discharging buffering mechanism, an automatic dyeing mechanism, a reagent adding mechanism, an after-dyeing drying mechanism, a reagent storage mechanism and a main console, the main console is in control connection with the feeding mechanism, the feeding and discharging buffering mechanism, the automatic dyeing mechanism, the reagent adding mechanism, the after-dyeing drying mechanism and the reagent storage mechanism, the feeding mechanism is provided with a plurality of microscope glass slide vertical placing supports, the automatic dyeing mechanism is provided with a dyeing groove, and the dyeing groove can be filled with a dyeing reagent and is matched with the microscope glass slides; the reagent adding mechanism can automatically add and supplement a dyeing reagent to the dyeing tank, the reagent storage mechanism can add and supplement the dyeing reagent to the reagent adding mechanism, and the post-dyeing drying mechanism is matched with the feeding and discharging temporary storage mechanism and the automatic dyeing mechanism. The microscope glass slide has the beneficial effect that the risk of cross contamination between microscope glass slides filled with biological tissue sample slices can be avoided.

The invention discloses a cervical cytopathy detection method based on a hypergraph convolutional network. The method comprises the following steps: performing sliding window slicing processing, unsupervised image decomposition and dyeing normalization on a cervical cytopathy image, generating normalized image input, and constructing an image input sample set; a target detection model is constructed based on YOLO11, a block-level feature extraction network (BBMM) module is embedded to enhance the perception ability, and a sparse attention module is adopted to perform key region feature enhancement; constructing a hypergraph neural network HGNN module based on a Patch-level relationship, and extracting a structural relationship between cells; integrating an uncertainty quantification mechanism, and generating a confidence thermodynamic diagram; a front-end and rear-end separated diagnosis platform is built, image uploading, detection result display, frame selection correction and interactive management are supported, and whole-process auxiliary diagnosis is achieved; the method has the advantages of high detection accuracy, high interpretability, flexible deployment and the like, and is suitable for intelligent early screening and clinical auxiliary diagnosis scenes of cervical cytopathy.

The invention discloses a skin cancer pathological image analysis system and method based on a Transform model. The system comprises an image acquisition and quality control module, a multi-scale preprocessing module, a hierarchical feature extraction module, an intelligent diagnostic reasoning module, a knowledge graph aided decision-making module, a result output and feedback module and a federal learning training module. According to the method, multi-spectral image acquisition and quality control are carried out, multi-modal enhancement and dyeing standardization preprocessing are carried out, pathological features are extracted in a layered manner by using an improved Transform model, subtype identification and malignancy degree evaluation are realized in combination with multi-task learning, uncertainty is quantified by means of Monte Carlo dropout, cases and guidelines are associated through a knowledge graph, privacy is protected through federal learning, and the model is optimized. According to the scheme, the diagnosis efficiency and accuracy are greatly improved, the model interpretability is enhanced, various clinical scenes are adapted, diagnosis standardization is promoted, and improvement of basic medical capacity is assisted.

The invention relates to the technical field of biomedical engineering and digital image processing, in particular to a digital image enhancement method based on cytopathology. The method comprises the following steps: eliminating noise and artifacts caused by non-uniform dyeing, section folding and dust factors; the problem of dyeing difference caused by different scanning devices is solved; key areas including cell nucleuses and cell membranes are highlighted, so that the visual effect of a low-contrast image is improved; segmenting an area, including cell nucleuses and cytoplasm, of the cells, and extracting morphological characteristics; the detail resolution of a low-resolution image, especially the definition of a small cell structure, is improved; the brightness component Y is corrected to compensate for the difference of different dyeing methods and dyeing qualities in brightness response, then the chromatic value is adjusted according to the color correction factor to make the colors of the images more consistent, and finally, the images which are acquired by different dyeing methods and dyeing qualities and subjected to brightness and chroma correction are fused to make the color of the images more consistent. And a final corrected image is obtained.

The invention provides a nervous tissue identification method based on artificial intelligence, and relates to the technical field of medical intelligent detection. According to the nervous tissue identification method, nervous tissue region annotation is carried out on 452 HE-dyed section images of bladder cancer, pancreatic cancer and porta-bile duct cancer by using QuPath software. 70% is used as a training set, 10% is used as a verification set of a training process, and 20% is used as an independent test set to train the nnUnet model. The whole training process is combined with an active learning strategy to enhance the specificity of the model. By using the algorithm and the deep learning technology, the nerve peripheral infiltration in the pathology is accurately and quickly evaluated, the workload of manual identification is greatly reduced, and the diagnosis accuracy is improved. The machine identification of tumor peripheral nerve invasion has obvious practicability, importance, clinical diversity and actual clinical value, and shows that the deep learning technology improves the potential of tumor detection and diagnosis.

The invention provides a blood cell image recognition method based on a multi-scale Vision Transform model, and the method comprises the steps: constructing a multi-scale feature extraction architecture, combining the global modeling capability of a Transform network, carrying out the parallel processing of Patches of different sizes, and fusing the multi-scale information, thereby achieving the recognition of a blood cell image. The cells in the blood smear can be comprehensively perceived from microscopic details to macroscopic contexts, so that not only is the adaptability of the model to cell size difference and morphological diversity enhanced, but also the recognition stability under the conditions of cell overlapping and dyeing difference in a complex visual field is improved; the accuracy and calculation efficiency of cell classification and counting are effectively improved, and full-process automation from visual field quality evaluation to automatic cell recognition and counting is achieved.

The present invention provides a method for identifying clue cells in a leucorrhea microscopic image, which belongs to the technical field of clue cell identification, and comprises: firstly, pre-processing the stained leucorrhea microscopic image, including graying, medical image enhancement and noise removal. Through spectral feature analysis, a feature space is constructed and adaptive threshold segmentation is adopted to extract the cell area, and then edge detection and morphological operations are performed to accurately extract the complete outline of the cell. The morphological feature parameters such as cell area, perimeter, roundness, grayscale mean and variance are innovatively calculated, and a feature discrimination equation group is established in combination with the spectral feature vector to realize accurate classification and identification of cells. The identification results are post-processed to remove noise interference and perform connected area analysis, the number and proportion of clue cells are counted, and annotated images are generated; and the iterative optimization of the data set verified by medical experts solves the technical problem that the consistency of the diagnosis results is poor due to the subjective experience differences of doctors.

The invention provides an automatic tumor budding identification method fusing pathological staining migration and YOLO target detection. The method comprises the following steps: training Hamp by using a CUT model; a staining migration model is established from the E image to the immunohistochemical image, and the staining migration model is utilized to carry out Hamp; e, converting the dyed full-width pathological section into a cytokeratin dyed full-width pathological section; and performing tumor budding detection on the generated full-width pathological section dyed by the cytokeratin by using a TB-YOLO model improved on the basis of YOLOv10. According to the method, the CUT is combined for pathological image staining migration, the IHC image generated through TB-YOLO analysis is used for tumor budding detection, meanwhile, accurate translation and detection of positive signals are ensured, richer diagnosis information is provided for pathologists, the diagnosis accuracy is improved, the pathological examination cost of patients is reduced, and the purpose that only Hamp is used for diagnosis is achieved. And E, the tumor budding is accurately detected by the dyed image, so that the comparability of medical images in different dyeing modes and the applicability of a cross-dyeing method are improved.

The invention relates to the technical field of spermidine application, in particular to application of spermidine in preparation of medicine for preventing and treating doxorubicin-induced cardiotoxicity and myocardial aging. Cell proliferation experiments and mouse survival rate experiments show that spermidine can reduce death of myocardial cells and mice caused by adriamycin; a q-PCR experiment shows that spermidine can improve abnormal expression conditions of P53, P21, P16, BAX, TNF-alpha, IFN alpha, IL-10 and IL-18 in myocardial cells caused by adriamycin; a beta-galactosidase staining experiment shows that the spermidine can reduce the condition that the expression of a marker beta-galactosidase for myocardial cell senescence induced by adriamycin is increased; a Western Blot experiment shows that the spermidine can improve the condition that the P53 protein is abnormally increased due to the adriamycin; an Elisa experiment shows that spermidine can improve the condition that myocardial injury markers cTn-I and AST and inflammatory factors IL-1beta are abnormally increased due to adriamycin, and the myocardial injury markers cTn-I and AST and inflammatory factors IL-1beta are abnormally increased due to adriamycin. Therefore, spermidine can improve cardiotoxicity and myocardial aging induced by adriamycin.

The invention discloses a breast cancer HER2 prediction method based on multiple prompt learning. The method comprises the following steps: firstly, acquiring image features of a breast cancer WSI pathological image; then generating a multi-prompt descriptive text by adopting a large language model, and embedding learnable prompts into the descriptive text by using a multi-modal pre-training model to generate multi-prompts; using a text encoder to obtain multiple prompt features; performing aggregation by adopting a text-guided feature aggregation module to obtain category global visual features and dyeing degree global visual features, and inputting the category global visual features and the dyeing degree global visual features into a global feature fusion module for integration to obtain HER2 features; and finally, calculating the cosine similarity between the slice-level prompt feature and the HER2 feature, obtaining an HER2 prediction result of the breast cancer WSI pathological image, and performing optimization by using cross entropy loss. According to the method, semantic information related to text description can be effectively captured, the HER2 evaluation process of a pathologist is simulated, and the HER2 prediction accuracy is improved.

The invention discloses a reticulocyte recognition and grading system based on a blood smear, and particularly relates to the field of medical cell morphology examination, and the system comprises an image acquisition module, a staining normalization module, an instance segmentation module, an erythrocyte classification module, a reticulocyte grading module and a statistics output module. The image acquisition module adopts an automatic microscope platform to continuously scan the blood smear under a 100-time visual field, acquires a high-quality bright field channel image through an automatic focusing technology, and monitors image definition, contrast and illumination uniformity; the statistical output module is used for calculating the proportion and bluish violet intensity statistics of reticulocytes of each level, and generating a visual quality control result and a diagnostic report; according to the invention, full-automatic processing from samples to reports is realized, interpretability and high classification precision are both considered, subjective difference and omission ratio of manual microscopic examination are significantly reduced, and efficiency and consistency of clinical detection are improved.

The invention relates to the field of medical image processing, in particular to a lesion recognition method and system for non-staining biopsy cells, and the method comprises the steps: carrying out the imaging collection of the non-staining biopsy cells flowing at a high speed through the time sequence synchronous control of pulse laser and high-speed imaging, and obtaining an original cell image; performing image preprocessing on the original cell image to obtain a preprocessed cell image; performing multi-target cutting on the preprocessed cell image to obtain a standardized single cell image; extracting multi-scale features of the standardized single cell image based on a deep learning model to obtain a lesion recognition result corresponding to the non-staining biopsy cells; and based on a preset structured report generation algorithm and a statistical distribution modeling mechanism, performing structured integration on the lesion recognition result to obtain a structured cell diagnosis report. Through pulse laser imaging and deep learning feature extraction, automatic lesion recognition of non-staining biopsy cells is realized, a report is efficiently generated, and accurate diagnosis is assisted.

The invention provides an instance segmentation method and device for early intestinal cancer vessel infringement phenomenon analysis and a readable storage medium, and the method comprises the steps: constructing an end-to-end automatic process: firstly, carrying out the multi-magnification overlapping cutting of a digital pathological image, and carrying out the high-sensitivity instance segmentation through a Mask2form instance segmentation network; combining the overlapped masks by adopting an efficient union lookup algorithm, and recursively complementing incomplete instances in combination with an ROI (Region of Interest) expansion algorithm; hE, CD-31 and D2-40 dyeing full slice level alignment is realized through rigid-non-rigid-micro registration, and a three-mode instance level graph block is cut based on a registration result; and finally, fusing multi-modal features by using a hybrid expert network enhanced by a large pathological model, and dynamically screening expert paths to carry out true and false positive judgment. The invention provides an efficient technical scheme for early accurate diagnosis of intestinal cancer, and is suitable for pathological big data analysis and clinical auxiliary diagnosis.

In some examples, a method includes using a machine learning encoder to extract respective sets of machine learning (ML)-based features from respective images of cells that are dying and unstained. Cells of a first subset are at a first state of dying, and cells of a second subset are at a second state of dying. The method may include using a computer vision encoder to extract respective sets of cell morphometric features from the respective images. The method may include using the respective sets of ML-based features and the respective sets of cell morphometric features to generate respective multi-dimensional feature vectors that represent respective cell phenotypes. The method may include using the respective multi-dimensional feature vectors to correlate, to the first state of dying or to the second state of dying, a phenotypic difference between the cells of the first subset and the cells of the second subset.

The invention belongs to the technical field of medical image processing, and particularly relates to an esophageal cancer treatment effect and survival combined prediction method and system based on multiple modalities, and the method comprises the steps: obtaining a preoperative CT image and Hamp of an esophageal squamous cell carcinoma patient; e, the dyed digital pathological image, transcriptome data and clinical diagnosis and treatment information are preprocessed and subjected to feature extraction, and radiomics embedding representation, pathomics embedding representation and pipeline branch embedding representation are obtained respectively; the radiomics embedded representation, the pathomics embedded representation and the pipeline branch embedded representation are aligned and input into a multi-modal fusion module based on a multi-head self-attention mechanism for feature fusion, and fusion feature representation is generated; and based on the fusion feature representation, synchronously outputting a curative effect prediction result and a survival prediction result by using a multi-task output module, and evaluating model prediction performance by using a curative effect evaluation index and a survival analysis index.

The invention relates to a bacterial pneumonia pathogenic bacterium identification method based on an intelligent Raman technology, which comprises the following steps: S1, setting a type label according to a gram staining characteristic classification mode or the occurrence frequency of bacterial types in bacterial pneumonia infection, and selecting a known strain according to the type label, collecting Raman spectrum data of a known strain; s2, carrying out wave number extraction, background subtraction, smooth noise reduction and normalization preprocessing on the Raman spectrum data; s3, constructing a classification model according to an AlexNet model; s4, dividing the preprocessed Raman spectrum data into a training set and a verification set, and inputting the training set, the verification set and corresponding type labels into a classification model for training to obtain a trained classification model; and S5, acquiring Raman spectrum data of detected bacteria by using a Raman spectrometer, preprocessing the Raman spectrum data, and inputting the preprocessed Raman spectrum data into the trained classification model to obtain the type of the detected bacteria. The bacteria are identified by establishing the classification model, so that the operation complexity is reduced, and the identification time is shortened.

The invention provides a multi-modal kidney pathological picture processing method and device, electronic equipment and a medium. The method comprises the following steps: acquiring an immunofluorescence picture included in a to-be-processed multi-modal kidney pathological picture and a PAS staining pathological picture corresponding to the immunofluorescence picture; inputting the immunofluorescence pictures into a pre-trained first network model to perform nephropathy type classification to obtain nephropathy types; judging whether the nephropathy type output by a pre-trained first network model belongs to a preset nephropathy type or not; inputting a PAS staining pathological picture corresponding to the immunofluorescence picture into a pre-trained second network model for pathological structure identification when the immunofluorescence picture belongs to the preset nephropathy type, and outputting a label for the identified pathological structure; and adding an Oxford typing label to the to-be-processed multi-modal kidney pathological picture based on a label and a pathological structure output by a pre-trained second network model. According to the scheme, the nephropathy type can be automatically distinguished, the Oxford typing label is added, the processing speed is high, and the accuracy rate is high.

The method and system for evaluating the curative effect of hepatocellular carcinoma based on immune microenvironment characteristics are high in accuracy and reliability of evaluating the curative effect of hepatocellular carcinoma. The method comprises the following steps: (1) preparing a sample; (2) carrying out multiple immunohistochemical staining; (3) image acquisition and analysis; (4) analyzing spatial distribution characteristics; (5) multiplying the input image voxel signal numerical value by the weight vector of the convolution kernel, transmitting the multilayer convolution kernel, and finally obtaining a calculation result output by each network; and (6) establishing a prediction model: based on the spatial distribution characteristics and clinical characteristics of immune cells, constructing the prediction model by using the positive rate of CD8 + PD-L1 + and CD103 + CD8 + cells in the total area and the number of CD103 + cells within 30 microns around tumor cells so as to predict the immunotherapy curative effect of the HCC patient.

The invention discloses a method for improving plant polyploidy induction rate and obtaining polyploidy based on leaf bud primordium cell cycle synchronization. The method comprises the following steps: carrying out pre-differentiation culture on an explant induction material; performing cell cycle synchronization excitation treatment on the leaf bud primordium cells obtained by the pre-differentiation treatment; and performing chromosome doubling treatment on the explant induction material subjected to the cell cycle synchronization excitation treatment. According to the method disclosed by the invention, an EdU staining labeling method and a flow cytometry are used for accurately judging the time for carrying out cell cycle synchronization excitation treatment on leaf bud primordium cells of an induction material, and carrying out synchronization excitation and doubling induction on the induction material in time; the dosage of a cell cycle inhibitor for synchronous excitation treatment is accurate, the treatment opportunity is accurate, the treatment duration is proper, and the polyploidy inductivity of the plant is remarkably improved; the obtained polyploidy plant is good in stability, and the method is high in repeatability.

The invention belongs to the technical field of tumor micro-immune environments, and particularly relates to a kit for detecting a tumor micro-environment, a detection method and application, the kit comprises rare metal element labeled monoclonal antibodies, the rare metal element labeled monoclonal antibodies comprise 115In-CD20, 139La-Histone H3, 142Nd-CD56, 144Nd-CD14, 146Nd-Vimentin, 148Nd-Pan-CK, 151Eu-CD31, 156Gd-CD4, 158Gd-E-Cadrin, 159Tb-CD68, 162Dy-CD8a, 166Er-CD45, 167Er-CD15, 170Er-CD3, 171Yb-CD11c and 209Bi-alpha-SMA, and the rare metal element labeled monoclonal antibodies comprise The invention can be used for simultaneously detecting and researching the quantity, positioning and spatial topological relation of each cell component in a tumor microenvironment, and provides a multi-immunofluorescence detection kit and method for ubiquitous epithelial differentiation-derived tumors based on an imaging mass spectrum flow technique. A plurality of biomarkers can be analyzed at the same time by using multiple immunohistochemical staining and rare earth metal tag signal detection technologies, so that the aim of performing high-dimensional analysis on a tumor microenvironment is fulfilled according to cell composition, functional state and cell-cell space interaction relationship.

The invention discloses a verification method for regulating and controlling proliferation and differentiation of bovine adipocytes by gene interference, which comprises the following steps: cutting bovine adipocyte tissues into small blocks, directly pasting the small blocks in a culture dish for culture, and promoting cell growth and proliferation by using nutrition and growth factors of the tissues; a ZNF423 gene is targeted by adopting an siRNA technology, siRNA is transfected into bovine fat cells by utilizing a transfection reagent, and the siRNA is an interference fragment containing si-ZNF423; the method comprises the following steps: respectively extracting total RNA (Ribonucleic Acid) and total protein of bovine adipocytes, and detecting by utilizing qPCR (Quantitative Polymerase Chain Reaction) quantitative analysis, a WB (White Batch) technology, EdU dyeing, flow cytometry detection, oil red O, BODIPY (Boron Dipyrromethene), Nile Red dyeing, TG (Triglyceride) content determination and the like. According to the invention, ZNF423 gene expression is interfered by the interference fragment containing si-ZNF423, and the effect of inhibiting proliferation and differentiation of bovine adipocytes is detected by various methods, so that the effect of the interference fragment in the bovine adipocytes is known conveniently, and regulation and control of proliferation and differentiation of the bovine adipocytes are realized by interfering gene expression by the interference fragment. The beef quality can be improved; the beef cattle breeding can be optimized.

The invention provides a breast cancer HER2 immunohistochemical digital slice automatic interpretation method, device and equipment and a storage medium. Relates to the technical field of digital pathological image analysis. Acquiring a full-slice image, automatically identifying an external contrast area and a tissue area through a multi-task head real-time instance segmentation model, and segmenting an ROI (Region of Interest) area of tumor cell aggregation; extracting image blocks by adopting a fixed sliding window based on the ROI region, and constructing a double-branch multi-task feature extraction model containing a backbone network: realizing HER2 expression pattern classification by an image block classification head, and outputting a continuous protein expression intensity quantized value by a dyeing intensity numerical head through regression analysis; and establishing a slice-level interpretation model, integrating the classification results and the intensity quantized values of all the image blocks, and outputting an automatic HER2 interpretation result. According to the invention, end-to-end analysis from cell-level characteristic quantification to slice-level diagnosis is realized, and an objective and quantifiable intelligent interpretation scheme is provided for HER2 immunohistochemical evaluation.

A dyeing-free imaging method for a plasmon tissue section based on a generative adversarial network relates to the technical field of biomedical detection, and comprises the following steps: 1) recording the tissue section placed on a plasmon glass slide by using a high-resolution bright field microscope and passing through a Hamp; e, dividing the dyed tissue slices into a training set and a verification set in proportion; 2) amplifying the number of paired images in the training set to a plurality of times through data enhancement; 3) constructing a GAN model, and training the GAN model by using the paired image after data enhancement as a data set; and 4) based on the trained GAN model, performing virtual staining of the staining-free imaging image of the plasmon tissue section. According to the invention, the generated virtual dyeing image is visually and statistically compared with the real Hamp; the E-dyed image has high similarity, and provides pathological interpretable images and more effective discrimination information for pathologists. The time is remarkably shortened, the cancer diagnosis efficiency is improved, and a rapid and effective tissue slice analysis means is provided for clinic.

The invention discloses a tomato LACS2 gene promoter and application thereof, and belongs to the technical field of biology. On one hand, the invention provides the tomato LACS2 gene promoter, and on the other hand, the invention provides application of the tomato LACS2 gene promoter in promoting target gene expression in plants. According to the invention, a tomato LACS2 gene promoter fragment (788bp) is constructed to drive an EGFP-GUS (Enhanced Green Fluorescent Protein-Glucuronidase) gene fusion expression vector. GUS histochemical staining and laser confocal microscopy observation prove that the promoter fragment can guide normal expression of the reporter gene in the tomato epidermis. Therefore, the LACS2 promoter can be used for researching development, quality improvement and postharvest preservation of tomato peels and specific expression of exogenous genes in the tomato peels.

The invention discloses a UNET supervised network slice HE virtual staining method and system for pathological analysis, and belongs to the technical field of artificial intelligence digital pathological analysis. Global scanning imaging is performed on a pathological slice to obtain an HE scanning image and a fluorescence confocal scanning image, and a UNET supervised network data set is established; a UNET supervised virtual dyeing network model is constructed; training and verifying the UNET supervised virtual dyeing network model by using the UNET supervised network data set; and carrying out HE virtual staining on the fluorescence imaging pathological section image by using the trained UNET supervised virtual staining network model. The method has the remarkable advantages in the aspects of dyeing precision, dyeing efficiency and robustness, and automatic and rapid virtual dyeing of different types of biological tissue sections can be achieved without accurate image registration.