208 results about "Tumor detection" patented technology

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention discloses an MRI medical image segmentation method and system based on deep learning, and the method comprises the steps: obtaining a mammary gland MRI image, carrying out the standardization processing of the mammary gland MRI image, and removing a non-region of interest to obtain a target MRI image; performing detail enhancement on the suspected lesion area to reconstruct an image to be preprocessed; performing noise reduction processing, contrast enhancement and edge enhancement preprocessing on the to-be-preprocessed image to generate three versions of input images, and inputting the three versions of input images into a pre-trained deep learning model as three channels to complete breast tumor identification; and carrying out image segmentation on the breast tumor region, and classifying segmented breast tumors. According to the MRI medical image segmentation method and system based on deep learning provided by the invention, accurate and efficient processing of the mammary gland MRI image is realized, efficient identification, accurate segmentation and accurate classification of the mammary gland tumor are realized, the accuracy and robustness of mammary gland tumor detection can be improved, and efficient and reliable auxiliary diagnosis is provided for clinic.

The invention discloses an AIE type fluorescent probe with tumor targeting as well as preparation and application thereof, and belongs to the technical field of medicines, the probe can accurately target HDAC6 high-expression tumor cells, the specific binding capacity of the probe reduces interference to normal cells, and the detection accuracy is improved. The probe is fused with AIE performance dye molecules and emits strong fluorescence during aggregation, so that the imaging sensitivity and definition are remarkably enhanced. By optimizing the structure and synthesis conditions, the fluorescence intensity and stability of the probe are improved, the sensitivity of early tumor detection is ensured, and the survival rate and life quality of patients are improved. The preparation method is simple, convenient, feasible, low in cost and beneficial to practical application. The probe can be used for preparing tumor detection products, assisting in tumor diagnosis and classification, monitoring treatment changes and evaluating the curative effect. High-precision imaging can be realized in the aspect of tumor imaging, the method can be used for evaluating curative effect and guiding formulation of subsequent treatment schemes, and the application prospect is wide.

The invention provides a tumor detection data analysis method based on a peripheral blood immune marker, and belongs to the technical field of immunodetection.The tumor detection data analysis method comprises the steps that firstly, a peripheral blood sample of a patient is collected, and plasma and cell components are separated; analyzing various immune cell phenotypes by using flow cytometry; determining the concentration of key cell factors; analyzing the molecular expression level of the immune checkpoint; establishing an immune cell subset proportion empirical function; analyzing a cell factor expression mode by using an immune lineage transcriptome analysis model; establishing a tumor immune microenvironment score by combining the immune cell subset proportion and a model output result; the accuracy of the scoring system is verified through clinical follow-up visit and iconography examination; a standardized report template is formulated to provide a tumor early detection basis. Wherein the immune lineage transcriptome analytical model adopts a multi-modal attention fusion network architecture, three different immune data streams can be processed and integrated at the same time, and accurate evaluation of tumor-related immune states is realized.

The invention discloses a bioelectrical impedance tumor detection method based on pattern recognition, and the method comprises the following steps: S1, carrying out the electrical impedance measurement of a to-be-detected tumor region through a bioelectrical impedance analysis instrument, and obtaining the current, voltage and impedance values at different depths and frequencies; s2, designing a feature extraction module, and extracting key features in the electrical impedance data of each channel under different frequencies; s3, inputting the extracted key features into a multi-level feature fusion module, and fusing the electrical impedance features under different channels and frequencies to form new fused features; and S4, inputting the fusion features into a self-adaptive classification module, and adjusting model parameters in combination with a self-adaptive optimization method. According to the invention, automatic processing and classification are carried out on data through a pattern recognition algorithm, and the accuracy, sensitivity and reliability of tumor detection can be effectively improved in combination with the feature extraction module, the multi-level feature fusion module and the adaptive classification module.

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 relates to the technical field of tumor detection, and discloses a tumor detection method and system and computer equipment, and the method comprises the following steps: obtaining autofluorescence decay data of a target biological tissue, and extracting fluorescence intensity and fluorescence lifetime data from the autofluorescence decay data; performing data fusion on the fluorescence lifetime data and the fluorescence intensity data of the target biological tissue to obtain dual-channel fusion data; and inputting the dual-channel fusion data into a tumor detection model pre-trained based on a deep learning network model, outputting the prediction probability of each pixel corresponding to different categories, taking the category with the highest probability value as the tumor detection result of each pixel, and obtaining the distribution positions of tumors and blood vessels in the target biological tissue. According to the method, the fluorescence lifetime data and the fluorescence intensity data are fused and then input into the tumor detection model, so that the model can analyze tissue characteristics from richer and more comprehensive dimensions, the distribution positions of tumors and blood vessels in target biological tissues can be more accurately positioned, and the detection accuracy and the recognition speed are improved.

The invention discloses a breast image segmentation method based on dynamic sampling and multi-scale parallel attention, and the method carries out the related work on a breast cancer core needle biopsy full-slice data set and a mammary gland slice data set built by a company, and carries out the training and verification of a mammary gland image segmentation model through a mammary gland tissue image data set. And predicting a to-be-detected mammary gland pathological section by using the trained segmentation model weight to obtain the specific position and type of the mammary gland abnormal tissue. According to the method, high-resolution features can be continuously reserved in the whole segmentation process, expression of important features is enhanced through a proposed attention mechanism, background information is effectively inhibited, detail and global information is captured under multiple scales, and the tumor detection precision of the model is effectively improved.

The invention discloses a medical image weak supervision variation segmentation method based on single-point labeling, and relates to the technical field of tumor segmentation in medical images. The method comprises the following steps: firstly, randomly taking a point in a target tumor area, and constructing a new anisotropic Riemannian metric according to texture direction information of a whole CT image, local direction information of gray scale at a boundary and heterogeneity of the gray scale; secondly, calculating two distances from all points in the whole CT image to the points taken in the tumor area: geodesic distance and Euclidean distance based on new Riemannian metric; then, the two kinds of point-by-point distance information are embedded into a Chan-Vese model; and finally, proving the existence and uniqueness of a model solution, and solving the model solution by adopting a Douglas-Rachford algorithm to obtain a final tumor segmentation result. According to the method, a more accurate, more effective and more robust segmentation result can be obtained, the method can be used for tumor detection, positioning and segmentation, the workload of manual labeling can be greatly reduced, and the method has high clinical application value.

The invention discloses an enhanced interpretable multi-mode esophageal cancer target detection method and system, and the method comprises the steps: collecting a medical image of an energy spectrum CT detection esophageal tumor, and carrying out the marking processing of the obtained CT image; according to the marked CT image, carrying out multi-modal classification on the medical image and the clinical data information; constructing a fusion model based on multi-modal deep learning to perform fusion processing on the multi-modal features obtained by classification; according to fusion features obtained by fusion, generating an esophageal tumor detection report based on weak supervision contrast learning; constructing an intelligent detection cloud platform by taking the optimized AI diagnosis model as a kernel; the intelligent detection cloud platform is used for transmission, storage management, image data preprocessing, automatic detection and query statistics of energy spectrum CT images. According to the embodiment of the invention, the accuracy and robustness of image segmentation can be improved, efficient and accurate automatic detection of esophageal tumors is realized, and the method can be widely applied to the technical field of computers.

Provided in the present invention is a qualitative assessment and data scoring system for predicting prostate cancer in a PSA gray zone. The system comprises a magnetic resonance device, wherein a port thereof is electrically connected to a data temporary storage module, the data temporary storage module sends data to an imaging detection module by means of a network connection module, the imaging detection module is connected to a background system, and the background system comprises a scoring module and a PC unit; the scoring module acquires the value of the volume ratio of central gland prostate to total prostate on the basis of magnetic resonance imaging, so as to obtain a scoring result; and the scoring module is connected to an association result output module and a storage module, the storage module is used for storing the scoring result, and the association result output module is used for matching the scoring result with a disease risk, and outputting a matching result. The present invention can improve the accuracy of tumor detection, thereby reducing unnecessary prostate puncturing.

The invention provides a tumor real-time tracking and benign and malignant detection method based on a mammary gland ultrasonic video, and the method comprises the steps: collecting the mammary gland ultrasonic video in real time, and inputting the mammary gland ultrasonic video into an UltraNet basic detector to generate a detection result of each frame of ultrasonic image; calculating a similarity probability matrix Si, j to reflect the similarity between the ith target and the jth target; performing cascade matching on the target based on the similarity probability matrix Si, j to form a target track pipeline Ti; and carrying out confidence coefficient updating and bounding box position updating on the detection result in each target track pipeline Ti, and finally outputting a breast lesion detection result of each frame of ultrasonic image. The tumor real-time tracking and benign and malignant detection method based on the breast ultrasonic video has the advantages of high calculation efficiency, high precision and strong robustness, can assist a doctor in accurately positioning a focus and performing diagnosis, and is beneficial to early discovery of breast diseases.

The invention provides a tumor recognition method based on multi-modal feature fusion, relates to the technical field of tumor recognition, and is used for solving the problems of missing multi-modal feature calibration, insufficient semantic association, poor generalization and insufficient clinical interpretability in the prior art. The method comprises the following steps: firstly, synchronously acquiring visual structure and electrical impedance bimodal data through an event-driven sensor array, and completing preprocessing through pulse coding, STDP rule optimization and quality verification; then, respectively extracting structured feature vectors of a tumor visual structure class and characteristic feature vectors of a numerical attribute class by a heterogeneous twin coding engine, and unifying dimensions and distribution through processes such as multi-scale dynamic perception and cross-modal calibration; then based on an attention mechanism, InfoNCE contrast loss and minority class weight gain, dynamic weighted fusion and semantic association enhancement are performed on the bimodal features, and a comprehensive feature vector is generated; and finally, through clinical logic adaptation and multi-center deviation correction, outputting a tumor benign and malignant identification result through a full-connection classifier, and synchronously generating a clinical interpretable report containing key features and weights. According to the method, the complementary advantages of bimodal information are effectively integrated, the problems of heterogeneous multi-center equipment, unbalanced samples and the like are solved, the tumor recognition accuracy and the early-stage tiny tumor detection rate are improved, the computing power consumption is reduced, edge medical equipment deployment is adapted, and the requirements for low misjudgment and traceability of clinical diagnosis are met.

The present invention discloses a method for detecting anti-tumor by bioelectrical impedance based on pattern recognition, which includes the following steps: S1: Use a bioelectrical impedance analysis instrument to measure the impedance of the tumor area to be detected, and obtain the current, voltage and impedance values at different depths and frequencies; S2: Design a feature extraction module to extract the key features in the impedance data at different frequencies of each channel; S3: Input the extracted key features into a multi-level feature fusion module to fuse the impedance features at different channels and frequencies to form new fused features; S4: Input the fused features into an adaptive classification module and adjust the model parameters by combining an adaptive optimization method. The present invention can automatically process and classify data through a pattern recognition algorithm, and at the same time, by combining a feature extraction module, a multi-level feature fusion module and an adaptive classification module, it can effectively improve the accuracy, sensitivity and reliability of tumor detection.

The invention discloses a sampling device for tumor tissues, and relates to the technical field of tumor detection. The device comprises a needle tube, the top of the needle tube is provided with a wire groove, the periphery of the bottom of the needle tube is fixedly provided with a connector, the bottom of the needle tube is fixedly connected with an operation cylinder in a threaded mode, the operation cylinder is internally provided with a sampling assembly, and the operation cylinder is internally provided with a suction assembly. The suction assembly is used for driving the sampling assembly to sample tumor tissues, the top end of the needle tube adopts a closed design, and the wire slot penetrates through the outer wall of the needle tube. In the process of puncturing the tumor tissue at one time, the tumor tissue can be sampled in different positions and different directions, the sampling difficulty is reduced, meanwhile, repeated puncturing is not needed, the sampling efficiency is improved, damage to a patient is reduced, after sampling is completed, the sampling barrel can be sealed, and the sampling efficiency is improved. The pollution of the external environment to the sample is avoided, and the accuracy of the detection result is ensured.

The invention provides a high-precision lightweight breast ultrasonic tumor detection method based on Improved-YOLOv8, and the method comprises the steps: obtaining a breast ultrasonic image, carrying out the preprocessing, and obtaining a preprocessed image; performing dynamic data enhancement to obtain an enhanced image; a fast network Faster Net and a self-adaptive wavelet down-sampling layer AWFD are introduced, a backbone network AWFD-Faster Net of an Improved-YOLOv8 network extracts features of an enhanced image, and then three image features are output to a fusion network Fusion; the fusion network Fusion carries out feature fusion on the input image features and then outputs three fusion features to the detection head network Head; the detection head network Head outputs an ultrasonic breast tumor detection result; the method has the advantages of short operation time, high calculation efficiency, high detection precision and the like, and meanwhile, the method is small in network parameter quantity and low in calculation complexity.

The present invention discloses a terahertz image breast tumor detection method based on SAM and curriculum learning. In order to accurately locate and identify tumors from terahertz breast images, the present invention applies the visual basic model SAM to tumor detection and constructs a dual-branch tumor detection network, in which one branch uses the target detection model DETR to perform coarse-grained tumor detection on terahertz breast images and generate mixed prompts, including bounding boxes and categories, where the categories are used as text prompts, and then input them into the SAM branch for fine-grained tumor detection. In addition, the present invention adopts the idea of ​​curriculum learning and sends terahertz breast tumor image data sets from easy to difficult into the dual-branch tumor detection network in batches for training to obtain better detection results.

The invention relates to an early tongue cancer latent lymph node metastasis prediction system, electronic equipment and a storage medium. The early tongue cancer latent lymph node metastasis prediction system comprises a data acquisition module used for acquiring head and neck MRI images, the head and neck MRI images being acquired MRI images of a target object; the automatic tumor detection model is used for performing target detection on the head and neck MRI image to obtain a tumor area image; the lymph node metastasis prediction model is used for performing lymph node metastasis prediction classification on the tumor area image to obtain a lymph node metastasis classification result; by means of the scheme, accurate tumor positioning of early oral tongue cancer can be achieved through the automatic tumor detection model, and accurate prediction of latent lymph node metastasis can be achieved through the lymph node metastasis prediction model.

The embodiment of the invention provides a cancer computer-aided diagnosis method and system, an esophageal cancer computer-aided diagnosis method and a gastric cancer computer-aided diagnosis method.The tumor detection method comprises the steps that a CT image processing task is received, the CT image processing task carries a plurality of CT images corresponding to a target detection area, and the target detection area corresponds to the CT images; the CT image processing task is used for detecting whether a tumor exists in the target detection area; the multiple CT images are input into a CT image processing model, a detection result corresponding to the target detection area is obtained, the CT image processing model generates the detection result corresponding to the target detection area based on the multi-scale feature information corresponding to the multiple CT images, and the detection result corresponding to the target detection area is obtained. The detection result comprises detection annotation information, detection category information and a detection guidance text. The accuracy of subsequently generated detection results is improved by obtaining the multiple pieces of scale feature information corresponding to the CT image. The detection result comprises the position information of the abnormal object, the information of the to-be-detected object and the guidance text, the detection result is enriched, multi-dimensional detection information is provided for the user, and the use experience of the user is improved.

The invention provides an ultrasonic breast tumor recognition method and device based on prior anatomical knowledge and a medium, and the method specifically comprises the steps: firstly segmenting an original breast ultrasonic image through a segmentation algorithm model, and carrying out the morphological processing of a segmentation result, thereby obtaining a breast region image; then performing channel synthesis on the original ultrasonic image and the breast region image to obtain a fused image, and finally performing breast tumor detection on the fused image by using the optimized target detection algorithm model. The interference of a non-tumor area can be greatly reduced through a prior knowledge mask image and original image fusion preprocessing mode, and the false positive rate of tumor recognition is reduced. Meanwhile, a target detection algorithm is improved by using large-scale convolution and programming gradient information, tumors of different scales and peripheral information can be better obtained, and the tumor detection effect is improved.

A device for hybrid ultrasound and optical imaging for early cancer detection, comprising: • an ultrasound imaging module that can capture high-resolution structural images of soft tissues for real-time visualization of suspected lesions; • an optical imaging module comprising at least one of photoacoustic imaging (PAI) and diffuse optical tomography (DOT) to acquire functional and molecular imaging data, including hemoglobin concentration, oxygen saturation and metabolic activity of tissues; • a multimodal image fusion machine configured to integrate ultrasound and optical image data to provide comprehensive, high-contrast diagnostic visualization; • a data processing unit with an AI-powered machine learning module trained on large cancer datasets for automatic tumor detection, segmentation, classification, and malignancy prediction; • a communication interface for real-time data transmission that supports both wireless and wired connections to enable remote monitoring, data exchange, and cloud-based AI analytics; • An interactive user interface that provides real-time visualization of diagnostic imaging results, alerts, and decision support tools for clinicians, with the interface enabling medical staff to adjust imaging parameters and thresholds for optimized diagnosis.

The invention provides a prediction or diagnosis system for myeloid tumors and application, and belongs to the technical field of tumor detection. The research finds that the intensity levels of Raman peaks 786 and 1579 cm <-1 > representing nucleic acid, Raman peaks 643, 759, 103, 126, 1603 and 1616 cm <-1 > representing protein, Raman peaks 1437, 1443 and 1446 cm <-1 > representing lipid, Raman peak 957 cm <-1 > representing beta-carotene and Raman peak 1345 cm <-1 > representing collagen in serum are detected as biomarkers, and the serum can be used for predicting or diagnosing myeloid tumors and subtypes thereof. The myeloid tumor and the subtype thereof can be quickly and noninvasively predicted or diagnosed by utilizing the myeloid tumor prediction or diagnosis system, more choices are provided for quickly predicting or diagnosing the myeloid tumor and the subtype thereof, and the system has profound scientific research and clinical significance.

The invention provides a MaskRcnn-based tumor detection method and system, and the method comprises the steps: inputting medical image data into a MaskRcnn network integrated with an edge perception module, generating an edge response graph through a Sobel operator, and enabling the edge response graph to serve as an additional channel injection feature graph, and enhancing the boundary representation; fusing multi-scale features through a feature pyramid network FPN, extracting candidate tumor area features through RoI Align, introducing non-local attention modeling global dependence in the area, extracting directional entropy, texture energy, uniformity and contrast in combination with a gray level co-occurrence matrix GLCM, and splicing to generate structure sensing features; performing classification, bounding box regression and mask segmentation based on the structure perception features, and constructing a joint loss function including classification, regression, mask cross entropy, edge alignment and structure consistency; the positioning capability of the model on the fuzzy tumor contour is effectively improved, the boundary positioning error is remarkably optimized, and the defect that over-segmentation or missing detection is likely to occur in a traditional model is overcome.

This invention relates to the field of data analysis technology, specifically to a cloud-based tumor detection data analysis and sharing system. The invention includes a cloud service platform and multiple local terminals. The local terminals communicate with the cloud service platform via a network and are deployed in various medical institutions. They digitize tumor detection samples to generate raw medical data and extract metadata. The cloud service platform includes a data receiving module for receiving raw data and metadata; a template management module for building and maintaining a feature template library, including a two-stream ensemble classifier; a data calibration module for calling standardized parameter sets from metadata and generating standardized data through a reversible neural network; a data analysis module for analyzing the standardized data; and a data sharing module for responding to requests to share data. The system solves the problem of heterogeneity in multi-center data, improves the model's generalization ability and robustness, and achieves efficient data sharing and accurate analysis.

The invention relates to a radionuclide labeled trimer probe 68Ga-TRAP-(FAPI) 3 targeting FAP and a preparation method thereof, Trap is used as a trivalent chelating platform, and three FAPI targeting units are covalently connected by clicking a chemical linking arm to form a trimer structure; the invention further relates to application of the 68Ga-TRAP-(FAPI) 3 probe in preparation of imaging agents or therapeutic drugs for diagnosing FAP positive diseases, experiments prove that the trimer probe is good in affinity and high in stability, and in cell experiments and animal experiments, the uptake rate of FAP positive cells is kept at a high level within 240 minutes; preclinical and preliminary clinical applications show that the tumor detection rate of the < 68 > Ga-TRAP-FAPI 3 in various cancer models is superior to that of < 18 > F-FDG, and compared with < 68 > Ga-FAPI-04, the < 68 > Ga-TRAP-FAPI 3 is clearer to display focuses (particularly tiny metastases), higher in uptake intensity and particularly outstanding in delayed imaging, and the < 68 > Ga-TRAP-FAPI 3 can be used for preparing a medicine for treating cancer. And a novel drug candidate with better performance is provided for precise diagnosis and staging of tumors and subsequent radionuclide targeted therapy.

A prostate-specific membrane antigen (PSMA)-targeting conjugate having a phosphonate backbone, which is used for tumor detection after chelation of a diagnostic nuclide and is used for radiation therapy after being combined with a treatment-type nuclide. A conjugate nuclide chelation part is replaced with a fluorescent probe or a cytotoxic drug so that the PSMA-targeting conjugate can then be used for diagnosis and treatment of diseases.

The invention relates to a DNA methylation detection method and a reagent. Specifically, the invention relates to a method for diagnosing whether a tumor exists in a subject and whether a tumor postoperative minimal residual focus exists or not by detecting a methylation marker in free DNA in a sample from the subject to judge the methylation level of the DNA, and judging the postoperative prognosis of the subject suffering from the tumor. Methods of predicting postoperative recurrence or assessing the efficacy of a treatment on a subject having a tumor. The invention also relates to a marker, a kit and a detection reagent for the method.

The invention provides a microsatellite site stability detection method and an electronic device thereof. The microsatellite locus stability detection method comprises the following steps: S1) utilizing high-throughput targeted sequencing data of a sample to be detected to obtain feature data in a preset microsatellite locus set, the feature data at least comprising length variation of a short tandem repeat sequence and structural variation occurring at a microsatellite locus; according to the microsatellite stability detection method and device, the problem that in the prior art, a method for detecting microsatellite instability has limitation can be solved, and the microsatellite stability detection method and device are suitable for the field of tumor detection.

The invention relates to the technical field of tumor detection, and discloses a tumor detection sample storage device which comprises a base, a first shell and a second shell are rotationally connected to the base, height adjusting assemblies are slidably connected to the inner side walls of the first shell and the second shell, and limiting assemblies are arranged on the height adjusting assemblies. A supporting assembly is arranged on the inner side wall of the base, and an air source assembly is arranged in the base. An angle adjusting assembly is arranged in the base; when a medical worker places samples in the analysis device for storage, a plurality of sample test tubes can be respectively placed in the independent storage areas, the sample test tubes are sealed through the two shells, and then the sample test tubes are stored in a vacuum and low-temperature manner, so that the subsequent analysis and detection of the samples in the sample test tubes are ensured.

The invention provides a digestive tract tumor detection method and system, relates to the field of medical image detection, and solves the technical problem of low accuracy of tumor boundary detection and automatic positioning in a digestive tract tumor image acquisition process in the prior art. The method comprises the following steps: taking a pan-tumor region image as a training sample of a binary decision tree algorithm, constructing an objective function based on the training sample to obtain a first objective function value, and obtaining a depth threshold value and an optimal classification point of the training sample; optimizing the optimal classification point based on the candidate feature sound to obtain a second objective function value; an unknown digestive tract tumor image is accessed, image points of the digestive tract tumor image are marked through a trained binary decision tree algorithm and a regression forest algorithm, the position distribution probability of the image points is generated, a position distribution thermodynamic diagram is generated based on the position distribution probability, and a tumor core area is obtained through mean shift. And completing the separation of the tumor region and the non-tumor region. The application is used in the digestive tract tumor detection process.