7 results about "Chromosome maps" patented technology

This invention relates to the field of chromosome abnormality recognition technology, specifically to a chromosome abnormality detection method based on a multimodal large model. The method includes: constructing an image dataset and a text dataset; fusing image features and text features to obtain multimodal fusion features; assigning anomaly scores to image blocks belonging to band regions based on anomaly scoring rules formulated from the multimodal fusion features, and comprehensively processing the anomaly scores of all image blocks corresponding to the chromosome to determine whether the chromosome image is abnormal; and decoding and generating natural language text that meets the requirements of chromosome abnormality detection based on the multimodal fusion feature representation and anomaly scoring rules. This invention achieves accurate chromosome abnormality detection and band location positioning through multimodal fusion and dynamic text generation mechanisms, generating interpretable natural language text descriptions, and improving the practicality and interpretability of the detection results.

This application provides a method and system for intelligent chromosome karyotype segmentation that integrates deep learning. The method involves: enhancing chromosome microscopic images; performing region segmentation based on the enhanced chromosome images to obtain multiple chromosome region sub-images; for any chromosome region sub-image, extracting the chromosome pixel area and band count corresponding to that sub-image, and determining the structural coverage index of the sub-image based on these values; comparing the structural coverage index with a preset chromosome banding distribution model to perform anomaly screening and identify candidate chromosome adhesion regions; and segmenting the candidate adhesion regions using a deep learning segmentation model to obtain the corresponding chromosome karyotype segmentation image. This application can screen and identify candidate adhesion regions based on the chromosome skeleton banding density, improving the accuracy of chromosome image segmentation.

The application provides a template image-based bone marrow abnormal chromosome detection method, device and medium. The method comprises: acquiring a bone marrow chromosome image to be detected; determining a target category of chromosomes in the bone marrow chromosome image through a trained and tested classification model; selecting a template image corresponding to the target category from a template library, wherein the template library stores a plurality of images representing normal bone marrow chromosomes; inputting the template image and the bone marrow chromosome image into a trained and tested difference analysis model to obtain a detection result, wherein the detection result comprises identification information representing whether the chromosomes in the bone marrow chromosome image are normal or abnormal, and an abnormal type corresponding to the abnormality when the chromosomes are abnormal. In this way, the accuracy of chromosome abnormality detection can be improved.

The application relates to a chromosome image segmentation and recognition method and system based on deep learning, and relates to the technical field of medical image processing.The method comprises the following steps: performing target detection processing on original chromosome image data to output chromosome image data with labeled chromosome clusters; using a dynamic convolution kernel and performing multi-level convolution and down-sampling processing on the chromosome image data to obtain intermediate layer down-sampling feature data and final layer down-sampling feature data; combining a parameter-free attention mechanism, performing deep separable convolution processing and pixel recombination processing on the final layer down-sampling feature data to obtain deep layer feature optimization data; performing inverse convolution processing and jump connection splicing processing on the deep layer feature optimization data and the intermediate layer down-sampling feature data to output chromosome segmentation image data; and performing classification and arrangement processing on the chromosome segmentation image data to obtain a chromosome karyotype analysis result.The chromosome image segmentation and recognition method and system can achieve good segmentation and recognition effects on chromosome images.

This application relates to a chromosome image processing method, apparatus, computer device, computer-readable storage medium, and computer program product. The method includes: acquiring an input image containing multiple chromosomes in metaphase; extracting individual chromosome images from the input image; performing feature analysis on each chromosome based on its image to obtain multidimensional chromosome feature information; and determining the phase quality assessment result of the input image based on statistical information corresponding to the multidimensional chromosome feature information. This method can improve the accuracy of chromosome image processing.

The chromosome classification processing method and system based on self-supervised contrast learning relates to the technical field of image processing; the chromosome classification processing method and system comprises obtaining a preliminary chromosome classification result; according to a self-defined homologous chromosome loss function, self-supervised contrast learning is performed on all first chromosome images and second chromosome images to obtain chromosome similarity analysis results of all first chromosome images and second chromosome images; the homologous chromosome loss function introduces a homologous similarity constraint term, and the chromosome similarity analysis results include the chromosome similarity of the first chromosome image and each second chromosome image; according to the preliminary chromosome classification result, the chromosome similarity analysis result and a priority matching rule, a final chromosome classification result is output, so as to solve the problem that the feature similarity of homologous chromosomes is not captured by the existing pre-training classification model, resulting in classification errors and affecting the accuracy of chromosome karyotype analysis.