30 results about "Tumor Pathology" patented technology

The invention discloses a multi-modal tumor identification method based on a frequency domain attention mechanism. The method comprises the following steps: S1, acquiring multi-frequency bioelectrical impedance signals of tumor tissues and normal tissues and corresponding medical tumor pathological images; s2, extracting a frequency-time feature of the electrical impedance signal and a spatial feature of the pathological image through a multi-modal feature coding module, and generating a multi-modal fusion feature; s3, converting the multi-modal fusion features into frequency domain representation, performing weighted enhancement on the multi-modal fusion features through a frequency domain attention module, and outputting the features; and S4, fusing the features output by the frequency domain attention module and the superficial layer features in the coding stage through jump connection to obtain enhanced features, inputting the enhanced features into a decoding network, and outputting a tumor classification result. According to the method, the bioelectrical impedance signal and the multi-modal information of the pathological image are fused, and a frequency domain attention mechanism is introduced to effectively improve the expression ability of key frequency characteristics.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention provides a method for inducing HL-60 cells to be differentiated into neutrophil-like cells, and belongs to the technical field of cell culture. According to the method, the induced differentiation efficiency of the HL-60 cells to the neutrophil-like cells (D-HL-60) and the cell quality are remarkably improved, high-proportion functional D-HL-60 cells can be obtained, a good motility rate can be maintained, and a stable and reliable cell model is provided for researching a neutrophil differentiation mechanism, screening related drugs and analyzing immune functions. Particularly, the functional D-HL-60 cells obtained on the basis of the method can be used as an ideal model, deep analysis of neutrophil trapping nets (NETs) in inflammation and tumor pathological mechanisms is assisted, and an experimental basis is provided for development of therapeutic strategies of targeted NETs.

The invention relates to the technical field of image recognition, and particularly discloses an auxiliary blood tumor pathological diagnosis system and method based on artificial intelligence, and the system extracts a pathological image group of a patient through a pathological diagnosis auxiliary platform, analyzes the data of each pathological image, and judges the effective feature value of each pathological image; the effective feature values of the pathological images are compared with a predefined effective feature threshold value to obtain a comparison result, and the pathological diagnosis auxiliary platform judges whether the effective features of the pathological images are enhanced or not based on the comparison result; and extracting cell characteristic data in each pathological image according to each pathological image, judging the characteristic complexity of each pathological image, and performing difference analysis on the pathological image group to complete auxiliary blood tumor pathological diagnosis.

The present patent application relates to nanoparticles for the delivery and targeting of a non-tumour-specific antigen in cancer cells, comprising a matrix support based on a biocompatible material, the non-tumour-specific antigen, and an adjuvant, for use in recalling, in cancer patients who have a specific immunity for the non-tumour- specific antigen pre-existing to the tumour pathology, the immune response specific to the non-tumour-specific antigen against the cancer cells. A further object of the application is anti-tumour pharmaceutical formulations comprising nanoparticles and kits comprising the aforementioned anti-tumour pharmaceutical formulations in combination with traditional anti-tumour vaccines.

The invention relates to negative functional modulators of at least one variant of the non-erythrogenic erythropoietin (V-EPO) and pharmaceutical compositions or kits containing them. Such functional negative modulators of V-EPO may be a mono- or multi-specific antibody anti-EV3, anti-EV4, anti-EV1-4, anti-EV1-5, anti-EV1-1, or EV2-1, or anti-Epo receptor (EpoR) anti-EPHB4, anti-CSF2RB, an antisense oligonucleotide, DNA decoy, RNA decoy, a ribozyme, an antagomiR, a shRNA, LNA or siRNA.Several uses of these functional modulators are described, which have been advantageously employed as a medicament and for the treatment of an oncological pathology, a proliferative pathology, chronic inflammatory diseases on an autoimmune and non-autoimmune basis, of neurodegenerative diseases, and in the treatment of patients undergoing organ or tissue transplantation.The invention also describes variants of EPO for use in the diagnosis and in the treatment of an oncological pathology, a proliferative pathology, chronic inflammatory diseases on an autoimmune and non-autoimmune basis, of neurodegenerative diseases, and in the treatment of patients undergoing an organ or tissue transplantation and as a diagnostic agent.According to another aspect, a monoclonal antibody to at least one of the variants of the erythropoietin is described.According to yet another aspect, the use of at least one alternative splicing variant of non-erythrogenic EPO and the measurement thereof at tissue and / or systemic level is described, as well as the study of the methylation status of the promoters of the genes involved in the EPO signalling pathway (by way of example EPO, EPOR, EPHB4, CSF2RB), as diagnostic, prognostic and predictive markers of an oncological pathology, a proliferative pathology, neurodegenerative or inflammatory pathology.

The invention relates to the technical field of tumor pathological specimen fixing and preservation, in particular to a tumor pathological specimen fixer which comprises a fixing box, a storage drawer is inserted into the fixing box, and a locking mechanism for conveniently limiting the drawing position of the storage drawer is arranged in the fixing box. An identifying and fixing mechanism which is convenient for fixing and identifying specimens is arranged in the storage drawer, and a taking and placing mechanism which is convenient for quickly taking and placing the specimens in a non-contact manner and is convenient for taking and placing the specimens is arranged above the storage drawer. According to the tumor pathological specimen fixing device, a tumor pathological slide specimen can be conveniently clamped, stored and fixed through a movable cavity formed in a storage drawer and a lifting rod, a fixing sleeve and a buffering cushion strip arranged in a lifting groove, and a sliding seat and a second magnet are slidably connected to a sliding rail fixedly connected to the bottom of an inner cavity of the movable cavity; and the second magnet slides below the first magnet, so that the first magnet and the second magnet are conveniently utilized to jack up the fixed sleeve and the taking and placing mechanism for observation and taking.

The invention provides a tumor pathological image typing and grading method and device based on deep learning and a residual network technology, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a tumor pathological image, segmenting a tissue region, carrying out the color normalization, and dividing the tumor pathological image into image blocks; constructing a deep convolutional feature extraction network based on a residual network, and extracting shallow and deep feature maps; performing up-sampling on the deep feature map and performing weighted fusion on the deep feature map and the shallow feature map, and inputting the deep feature map and the shallow feature map into parallel convolution branches to obtain typing and grading feature maps; calculating a feature deviation degree based on the hierarchical feature map to generate a space attention map, and determining a region focusing degree; enhancing the typing feature map by using the attention weight; respectively carrying out global pooling and classification on the enhanced feature map and the graded feature map, and outputting a typing and grading result; screening high-focus points according to an area focusing degree set threshold value, and generating a key area thermodynamic diagram; according to the invention, typing and grading cooperative processing is realized, and the diagnosis efficiency and accuracy are improved.

The invention discloses a brain tumor specimen space positioning and section manufacturing device, and belongs to the technical field of brain tumor pathological sections, and the brain tumor specimen space positioning and section manufacturing device comprises a bottom plate, a fixing frame, a directional puncture device, tumor forceps and a proofreading device. The system can automatically trigger and record the space coordinates at two key action points for clamping and releasing a tumor, accurately calculates the position and angle change of the tumor from the inside to the outside by calculating a space transformation matrix, solves the fundamental problem of in-vitro specimen orientation loss, and improves the accuracy of the in-vitro specimen. A two-stage posture adjusting system composed of a rotating assembly and an angle fine adjustment assembly is used for driving a puncture needle to conduct puncture and dyeing marking at the calculated accurate angle, the marking direction corresponds to the original direction of a tumor in a body, and a reliable space reference is provided for pathological analysis; the calibration device is arranged to perform multi-attitude high-precision calibration on the three-dimensional directional monitoring assembly of the tumor forceps.

The invention discloses an iodine contrast agent adverse reaction risk prediction method and system for tumor patients, and relates to the technical field of medical data processing and risk prediction. According to the technical key points, the method comprises the following steps of: constructing a tumor patient exclusive multi-dimensional time sequence data set, preprocessing data, constructing a tumor patient exclusive risk factor system, constructing a pharmacological perception drug embedding layer, constructing a multi-scale self-adaptive time sequence feature extraction core module, and coupling and modulating a tumor pathological state and a time sequence risk. Performing multi-feature fusion and risk prediction model training verification, risk prediction and interpretability clinical decision support and clinical iterative optimization of the model; by constructing a multi-dimensional data set, time sequence modes of different anti-tumor drugs in different short, medium and long risk periods are dynamically captured, and the specific pathological state of a tumor patient is used as a modulation factor to non-linearly amplify or weaken the risk intensity of a corresponding time window, so that the accuracy and clinical credibility of risk prediction are improved.

The invention relates to the technical field of tumor immunity, and discloses a sample treatment method for detecting a circulating mononuclear cell immune function and a tumor microenvironment macrophage immune function, which comprises the following steps: treating a peripheral blood sample and a tumor pathological tissue sample; according to the treatment method, the mononuclear cell purity is improved through CD14 magnetic bead sorting, the macrophage recovery rate is improved through mixed enzymatic hydrolysate, and the core improvement that mild reagents (a hypotonic hemolysis buffer solution and a BSA-containing stationary solution) protect the cell activity and the antigen integrity is achieved, so that the treatment method has the important significance in the aspects of the mononuclear cell purity, the macrophage recovery rate, the cell activity and the antigen fluorescence intensity. Compared with the prior art, the method provided by the invention has the advantages that the method provided by the invention has the advantages that the method provided by the invention is obviously superior to a conventional treatment method, the defects of low monocyte enrichment efficiency, poor macrophage recovery rate and insufficient cell activity and antigen integrity of the conventional method are overcome, and a higher-quality sample basis is provided for subsequent circulating monocyte subtype typing (Mo1-Mo4) and tumor microenvironment macrophage immune function detection.

The invention provides a tumor pathological image detection method. The tumor pathological image detection method comprises the following steps: sectioning an in-vitro tissue separated from a tumor focus along a preset plane to form a sectioning plane; the coloring agent is used for coloring the section cutting plane to form a to-be-detected object; translating, scanning and imaging along the profile contour of the to-be-detected object by using an imaging device so as to continuously obtain a plurality of local pathological images; and splicing the local pathological images according to the imaging sequence to form an annular image at least covering the profile contour, judging whether the annular image contains tumor cells or not through a machine learning algorithm, and judging that the in-vitro tissue contains a complete tumor when the annular image does not contain the tumor cells. According to the invention, the calculation cost for judging whether the annular image contains the tumor cells or not by a machine learning algorithm is remarkably reduced, and the detection time is remarkably shortened.

The present application relates to the technical field of necrosis region segmentation, and particularly relates to a kind of automatic segmentation method of digestive tract tumor pathological image necrosis region.The present application obtains the necrosis probability of each image block according to the pixel value distribution of different pixel points in each image block and the morphological characteristics of different regions;for any image block, according to the pixel value distribution of different regions, the necrosis probability and the preset pixel fixed step, obtain the new clustering center corresponding to the cytoplasm region in each image block, and the iteration clustering of all pixel points is carried out until the preset iteration number is reached, to obtain the optimized clustering center of cytoplasm region;according to the optimized clustering center corresponding to the cytoplasm region in each image block, clustering is carried out, the new necrosis probability of each image block is analyzed, and the necrosis block is screened out for segmentation.The present application improves the accuracy of segmentation by adaptively adjusting the clustering center in the clustering algorithm.

The invention provides a tumor prediction method based on a hierarchical visual language and electronic equipment, the method is applied to the field of artificial intelligence, and the method comprises the following steps: obtaining a three-dimensional medical image of a target patient and a tumor region of interest, key sub-region features of the region are extracted to obtain a local semantic feature set, tumor and adjacent tissue features are extracted to obtain a global context feature set, and the local semantic feature set and the global context feature set form a visual feature set; the method comprises the following steps: constructing clinical prior query of a target cancer species and an image mode, generating prognosis related language prompts through a large language model, encoding, solving an optimal matching plan matrix of a semantic matching optimal problem, and screening an elite feature subset. And enhancing a global feature set through gating fusion, applying a contrast learning consistency constraint, fusing features to form a representation vector, inputting the representation vector into a prediction layer, and obtaining a survival risk score. According to the method, priori knowledge and tumor pathological features can be fused, so that an accurate and reliable survival risk score is finally output, and the method is more reliable to adapt to clinical actual evaluation requirements.

The invention discloses a tumor chemotherapy effect prediction model construction method and system based on artificial intelligence. The method comprises the following steps: multi-dimensional data acquisition: acquiring tumor biological data, individual clinical heterogeneity data and patient report outcome data of a target cancer type patient; performing feature engineering processing: performing quantitative processing on the individual clinical heterogeneity data to generate quantitative indexes, and performing feature extraction on the tumor biological data to form a tumor microenvironment feature set; basic model training: taking multi-dimensional data of large-sample general cancer species as a training set, adopting a multi-branch feature fusion network to construct a general chemotherapy effect prediction basic model, and taking chemotherapy effect prediction and life quality influence prediction as double optimization targets for training, according to the invention, by collecting tumor biological data, individual clinical heterogeneity data and patient report outcome data, a multi-dimensional data system is constructed, and tumor pathological characteristics and microenvironment states can be comprehensively covered.

The application discloses a kind of multi-view child tumor pathological image classification deep network incremental learning method, this method is first to the child tumor pathological image feature modeling, then under the condition of constantly obtaining new data, training obtains incremental learning model, to improve its accuracy and obtain robust classification ability.The innovation of the present application lies in the characteristics of medical pathological image are designed, a multi-level knowledge distillation regularization method is proposed to alleviate the catastrophic forgetting problem of old model in incremental learning when adapting to new data, increase the robustness and reliability of algorithm.The method of the present application can be applied to the field of medical image processing and other tasks requiring incremental learning.

The invention relates to an adrenal cortical carcinoma prognosis evaluation method based on a peripheral blood inflammation marker, which comprises the following steps: acquiring a peripheral blood detection result and tumor pathological characteristics of a patient before treatment, and calculating a derived neutrophil-lymphocyte ratio and a platelet-leukocyte ratio based on the peripheral blood detection result; inputting the derived neutrophil-lymphocyte ratio, the platelet-leukocyte ratio, the age of the patient and the tumor pathological characteristics into a prognostic scoring model to obtain a risk score; wherein the prognosis scoring model is a regression model which is screened by adopting Cox regression analysis and is established with variables related to the postoperative disease-free lifetime. According to the invention, the accuracy of prognosis evaluation of the adrenal cortex cancer can be improved.

The invention discloses a slice consistency discrimination method and system based on a wax block and a pathological slice image, and relates to the technical field of tumor pathological diagnosis. The method comprises the following steps: receiving a to-be-compared wax block image and a to-be-compared pathological section image acquired by a color camera, and using a deep learning model YOLOv11 to realize positioning of a section image guided based on the tissue size in the wax block image and a tissue area in the wax block image; constructing an image alignment module and performing training to realize alignment of the wax block image and the slice image; and inputting the aligned slice and wax block images into the trained twin-tower twin neural network for consistency discrimination. According to the method, the wax block and the section image are automatically compared and analyzed through the deep learning model, technicians are assisted to quickly complete the quality control work of the pathological section, the mode of only depending on manual sampling inspection in the past is changed, the quality control efficiency is improved, the deep learning feature extraction method is adopted to carry out image alignment and judgment, and the detection accuracy is improved. And the accuracy of judging the consistency of the wax block and the slice is improved.

The invention provides a tumor pathology image classification method based on artificial intelligence, and relates to the technical field of image analysis, and the method comprises the following steps: obtaining tumor pathology image data, and carrying out the dyeing normalization processing of the tumor pathology image data, performing multi-scale feature extraction on the processed image data by adopting a multi-branch parallel feature extraction network, and performing feature fusion to obtain a multi-scale feature map; identifying and positioning a tumor lesion area according to the multi-scale feature map, and segmenting the lesion area from the image; the segmented lesion areas are classified according to lesion types, lesion levels of the classes are analyzed after classification, and a classification level diagnosis report is output; finer grading is realized, a more accurate diagnosis basis is further provided for doctors, and the accuracy and reliability of pathological diagnosis are improved.

The invention relates to a KIF11 and Ki67 immunohistochemical double staining kit and application, and belongs to the technical field of biological medicine. The invention provides a KIF11 and Ki67 double-staining kit for tumor pathological typing and prognosis evaluation. The kit comprises a first monoclonal antibody of an anti-KIF11 protein, a second monoclonal antibody of an anti-Ki67 protein, and a third monoclonal antibody of an anti-Ki67 protein, a second monoclonal antibody of an anti-Ki67 protein; a first species-specific second antibody corresponding to the first monoclonal antibody, wherein the second antibody is coupled with a first enzyme; a second species-specific second antibody corresponding to the second monoclonal antibody, the second antibody being coupled with a second enzyme different from the first enzyme; a first chromogenic substrate corresponding to the first enzyme, wherein the first chromogenic substrate develops a color I; and the second chromogenic substrate corresponding to the second enzyme develops a second color which can be distinguished from the first color. According to the invention, an independent prognosis index is provided by quantifying the proportion of the special subgroup KIF11-High / Ki67-High cells, and the kit has an important prognosis value.

This invention discloses a breast tumor pathology sampling device, relating to the field of medical devices. It includes an outer sampling tube and an inner sampling tube rotatably disposed within the outer tube. It also includes: a sampling mechanism for cutting and storing samples for tumor pathology; a driving mechanism for driving the inner sampling tube to rotate within the outer tube for sampling; and a negative pressure mechanism for cooperating with a sample storage chamber for tumor pathology sampling. This mechanism allows a negative pressure hose to connect to a negative pressure pump, creating a negative pressure environment within both the outer and inner sampling tubes to improve the efficiency of tumor pathology sampling. Furthermore, a storage tube can store blood from the tumor pathology sample. When the inner sampling tube absorbs the tumor pathology sample under negative pressure, the blood within the tissue is filtered and stored in the storage tube, while the tumor pathology sample is stored in the inner sampling tube. This not only efficiently and completely completes the tumor pathology sampling process but also prevents blood from affecting the tumor pathology sample.

The application discloses a kind of tumor tissue MPIF-HE image registration and cell segmentation method and system, the method includes: obtaining multiple immunofluorescence MPIF dyeing tumor tissue section image and pre-processing and multi-channel separation, obtain the single-channel MPIF image after pre-processing;Based on HE dyeing image extraction tumor infiltration front area as registration reference, carry out registration to HE dyeing image and the single-channel MPIF image after pre-processing, obtain the HE image segmentation result after registration;Based on the HE image segmentation result after registration, demarcate region of interest, and carry out segmentation and phenotype classification to the immune cell in this region of interest by pre-trained cell segmentation model, obtain cell segmentation result.The application can improve the cell segmentation precision of cell concentration and complex background.The application can be widely applied to tumor pathological image analysis technical field as a kind of tumor tissue MPIF-HE image registration and cell segmentation method and system.

The invention provides a tumor pathological image detection method and electronic detection equipment. The tumor pathological image detection method comprises the following steps: sectioning an in-vitro tissue along a preset plane to form a sectioning plane, and staining the sectioning plane by a staining agent to form a to-be-detected object; determining an annular detection area completely covering the profile contour based on the profile contour, performing twice division on the annular detection area along the radial direction to form a plurality of fan-shaped sub-detection areas, reserving part of the fan-shaped sub-detection areas intersecting with the profile contour, and performing translation scanning imaging on the reserved part of the fan-shaped sub-detection areas to continuously obtain a plurality of local pathological images, splicing the local pathological images according to the imaging sequence to form a to-be-detected image area at least covering the profile contour, judging whether the to-be-detected image area contains tumor cells or not through a machine learning algorithm, and judging that the in-vitro tissue contains a complete tumor when the to-be-detected image area does not contain tumor cells. According to the invention, whether the tumor area is completely cut off can be rapidly and effectively judged, and the detection time is remarkably shortened.

This invention discloses a fluorine-nitrogen co-doped carbon dot, its preparation method, and its applications. The fluorine-nitrogen co-doped carbon dot is a nanoparticle constructed using a one-step hydrothermal synthesis method with fluorine-containing and nitrogen-containing precursors. The nanoparticle is spherical with a particle size of 5 nm, and its surface is rich in carboxyl, amino, and hydroxyl functional groups. Its fluorescence quantum yield is not less than 20%, and its emission wavelength is in the 500-550 nm range. It has the characteristic of targeting the H3K36me2 histone modification in tumor cell nuclei, and can achieve specific labeling of tumor cells within 5 minutes. The enrichment intensity in tumor tissue is more than 2.72 times that in normal tissue. The fluorine-nitrogen co-doped carbon dot of this invention is expected to serve as a novel fluorescent labeling probe for tumor identification and imaging, contributing to the early and accurate diagnosis of malignant tumors. It has multiple advantages such as speed, accuracy, safety, and visualization, and has broad application prospects in tumor pathology identification and intraoperative navigation.

The invention discloses application of SNAI1 expression in glioma diagnosis, and relates to the field of medicine. The application is the application of the SNAI1 gene or the encoded protein thereof as a biomarker in preparation of a product for glioma diagnosis. Wherein the diagnosis comprises the step of judging whether a subject suffers from glioma or evaluating the malignant degree of glioma; when it is detected that the mRNA expression level of the SNAI1 gene or the expression level of the SNAI1 protein in the sample of the subject is higher than a normal control level, it is indicated that the subject suffers from the glioma or the malignant degree of the glioma is high. Analysis of a plurality of independent databases such as TCGA, GEPIA and GEO proves that the mRNA and protein expression levels of SNAI1 in glioma tissues (especially glioblastoma) are obviously higher than those in non-tumor tissues and low-level astrocytoma, and the expression levels are in positive correlation with tumor pathological grading.

The present invention relates to a compound 2-hydroxy-octadecene-9-cis-acid for use in the treatment of tumor pathology, the tumor pathologies include grade IV glioblastoma with natural IDH, grade 3 glioma, embryonic tumor, pituitary tumor, meningioma, vascular epidermis cytoma, vascular blastoma, mesothelioma, bile duct adenocarcinoma, exocrine pancreatic cancer, neuroendocrine pancreatic cancer, rectal cancer, umbilical urinary duct cancer, bladder adenocarcinoma, and esophageal cancer, as well as for the treatment of neuropathic pain. The neuropathic pain is caused by diabetic peripheral neuropathy, spinal cord injury, post-herpes zoster neuralgia, a chemotherapeutic agent, an antitumor agent, cancer, fibromyalgia or hepatitis. The present invention also relates to a pharmaceutical composition comprising 2-hydroxy-octadecene-9-cis-acid, and a pharmaceutical composition comprising 2-hydroxy-octadecene-9-cis-acid.

The utility model discloses a Raman spectrum oral cavity tumor pathological diagnosis analyzer, which belongs to the technical field of clinical application and comprises a spectrum diagnosis device and a sampling table, the sampling table is mounted on a storage cabinet, a cabinet door is mounted on the storage cabinet, a feed opening communicated with the inside of the storage cabinet is arranged on the sampling table, and an annular conveying mechanism is mounted in the storage cabinet. A plurality of storage boxes are placed on the annular conveying mechanism, each storage box is conveyed through the annular conveying mechanism to pass through the lower portion of the discharging opening, and a heat exchanger is further installed in the storage cabinet and electrically connected with the temperature controller. The discharge port is communicated with the storage cabinet, so that tissue samples sampled by the sampling table can be placed in the storage boxes for storage, and the plurality of storage boxes are conveyed by the annular conveying mechanism to pass through the lower part of the discharge port, so that the tissue samples can accurately fall into the storage boxes; and a plurality of tissue samples can be stored more conveniently and quickly.

The invention discloses a bone tumor pathological specimen sampling adjustable fixator, and relates to the field of medical auxiliary instruments, the bone tumor pathological specimen sampling adjustable fixator specifically comprises a base, a lifting seat, an inclined seat, a positioning ring and a fixing assembly, and is characterized in that the base is used for fixing the device and an operation table, and stand columns are fixedly mounted on the two sides of the upper end of the base; lifting seats capable of longitudinally lifting are arranged above the stand columns on the two sides, and the opposite faces of the two lifting seats are rotationally connected with the same inclined seat through slewing bearings; the upper ends of the two sides of the inclined base are each fixedly provided with two supporting columns through corner connectors. A pathological specimen is fixed to the middle of the positioning ring through the multiple fixing assemblies, compared with a manual fixing mode, the safety is better, meanwhile, during sampling, the front and back sampling angles of cutting-in can be adjusted through rotation of the inclined base, the positioning ring is designed to be rotatable, and the sampling efficiency is improved. And the pathological specimen can be rotated and adjusted clockwise or anticlockwise as required.

The invention relates to the technical field of tumor pathology diagnosis and evaluation, and discloses an artificial intelligence-based tumor pathology comprehensive diagnosis and prognosis evaluation system, which comprises a multi-source data acquisition module, a data processing module, a data processing module and a data processing module, the system is used for acquiring pathological images, clinical data and gene data of a patient from multiple channels such as a hospital information system and laboratory equipment, and a comprehensive data basis is provided for subsequent analysis. According to the tumor pathology comprehensive diagnosis and prognosis evaluation system based on artificial intelligence, multiple kinds of information such as pathology images, clinical data and gene data are obtained through the multi-source data acquisition module, a comprehensive data basis is provided for diagnosis, the feature extraction and selection module can extract features related to tumors from the data, and the diagnosis accuracy is improved. And through a feature selection algorithm, the most representative and distinguishable features are screened out, the data dimension is reduced, and the efficiency and accuracy of the model are improved.