6 results about "Multinomial logistic regression" patented technology

The invention provides an analysis method and system for a soil microbial community structure and a medium, and relates to the technical field of ecological environment monitoring. According to the method, environmental parameters, namely pH, organic carbon, moisture and oxidation reduction potential, of a soil sample are collected and subjected to normalization processing, phylum abundance, dominant phylum information and Shannon index classification community structure categories are obtained in combination with high-throughput sequencing, the support probability of the environmental parameters for classification is calculated through a multi-term logistic regression model, and the classification result is obtained. A D-S evidence theory is utilized to fuse multi-source BPA (basic probability allocation), joint confidence distribution is output, a conflict factor threshold value is set, an artificial review prompt is triggered under a certain condition to avoid errors caused by environmental parameters, finally, to-be-detected soil parameters are input into a pre-training model, confidence distribution is output in real time, and the detection accuracy is improved. And judging the structure of the microbial community in combination with a threshold rule, and outputting health, risk, transition state or uncertainty.

Disclosed in the present invention is a physician-interpretable ultrasound-based breast cancer pathological subtype detection system, comprising: a segmentation module, which uses a U-Net network and incorporates a foreground optimization network to segment a breast and a nodule; a texture channel, which extracts a texture channel feature; an edge channel, which extracts boundary information; a position channel, which extracts position information, so as to quantize the overall relationship between the nodule and the boundary information of the breast to extract a breast nodule growth position feature; a shape channel, which calculates an edge aspect ratio on the basis of a segmented nodule region, so as to obtain a predicted interval value as a shape channel feature; an echo channel, which calculates the echo difference between the interior and exterior of the nodule to serve as an echo channel feature value; and a joint prediction module, which uses a multinomial logistic regression model to classify and predict breast cancer pathological subtypes on the basis of output features of the texture channel, the edge channel, the echo channel, the shape channel and the position channel. An artificial intelligence diagnosis tool that is interpretable to physicians is built, thereby providing an automated theoretical basis for clinical diagnosis and decision-making.

The invention discloses a plasma exosome marker of tuberculosis (TB) and application of the plasma exosome marker, and belongs to the field of biomedical diagnosis, the plasma exosome marker comprises hsa-miR-451a, hsa-miR-1908-5p and hsa-miR-1268b, the expression of hsa-miR-451a in the plasma exosome of an active TB patient is up-regulated, and the expression of the hsa-miR-451a and the expression of the hsa-miR-451a and the expression of the hsa-miR-1908-5p are down-regulated. The expression levels of the markers are detected by using a droplet digital PCR technology, and a multinomial logistic regression diagnosis model is constructed based on a detection result. Experiments prove that when the three miRNA combinations are used for distinguishing active tuberculosis from healthy control (HC), the area (AUC) under a working characteristic curve of a subject reaches 0.970; when latent tuberculosis infection (LTBI) is distinguished from a healthy control, AUC is 0.971; and when the secondary miRNA combination of the hsa-miR-451a and the hsa-miR-1268b is used for distinguishing the asymptomatic tuberculosis (aTB) from the healthy control, the AUC is 0.880. The TB early diagnosis accuracy is remarkably improved, the non-invasive advantage is achieved, wide application prospects are achieved in the aspects of preparation of diagnostic reagents and kits, clinical diagnosis, disease monitoring and the like, and global tuberculosis prevention and control work is powerfully promoted.

The invention belongs to the field of deep learning and remote sensing image processing, and particularly discloses a hyperspectral few-sample classification network construction method and a hyperspectral ground feature classification method, and the method comprises the steps: obtaining a hyperspectral remote sensing image and sample label data thereof; performing cross-domain reconstruction and spectral curve extraction, and filtering out common information among categories through eigenvalue decomposition to obtain a discretized spectral curve; extracting physical invariance features, and taking the physical invariance features as constraint rules to generate virtual samples; the reconstructed hyperspectral data and the spectrum self-supervision auxiliary information are input into a double-branch variational automatic encoder network, multi-loss constraint of cross reconstruction is carried out, and the hyperspectral data and the spectrum self-supervision auxiliary information of the same category show greater similarity in a potential space; and outputting a final surface feature prediction result based on the multinomial logistic regression classifier, and completing the construction of the hyperspectral few-sample classification network. According to the invention, high-precision and high-robustness hyperspectral ground feature classification can be realized under the condition of sample scarcity.

The application belongs to the field of deep learning and remote sensing image processing, and specifically discloses a hyperspectral few-shot classification network construction method and a hyperspectral feature classification method, which comprises the following steps: acquiring hyperspectral remote sensing images and sample label data thereof; performing cross-domain reconstruction and spectrum curve extraction, and filtering common information between categories through eigenvalue decomposition to obtain discrete spectrum curves; extracting physical invariance features and taking them as constraint rules to generate virtual samples; inputting the reconstructed hyperspectral data and spectrum self-supervised auxiliary information into a double-branch variational autoencoder network to perform multi-loss constraint of cross reconstruction, so that the hyperspectral data and the spectrum self-supervised auxiliary information of the same category show greater similarity in the latent space; and outputting final feature prediction results based on a multinomial logistic regression classifier to complete the construction of the hyperspectral few-shot classification network. The application can realize high-precision and high-robustness hyperspectral feature classification under the condition of sample scarcity.

The invention discloses an ultrasound-based breast cancer pathological subtype detection system understandable by doctors, which comprises a segmentation module for segmenting a breast and a nodule by adopting a U-Net network and embedding a foreground optimization network; texture channel extraction: extracting texture channel features; the edge channel is used for extracting boundary information; the position channel is used for extracting position information so as to quantify the overall relation between nodules and breast boundary information and extract breast nodule growth position features; a shape channel, calculating an edge length-width ratio according to the segmented nodule region, and obtaining a prediction interval value as a shape channel feature; an echo channel: calculating an echo difference value inside and outside a nodule as an echo channel characteristic value; and the combined prediction module is used for carrying out classification prediction on the breast cancer pathological subtypes through a multi-term logistic regression model based on the output characteristics of the texture channel, the edge channel, the echo channel, the shape channel and the position channel. An artificial intelligence diagnosis tool which can be understood by a doctor is built, and an automatic theoretical basis is provided for clinical diagnosis and decision making.